docs/engineering_decisions.md

Engineering Decisions

ADR-style decision log. Each entry documents what was decided, why, and what was traded off.

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ED-001: Custom FL Engine vs Flower (flwr)

Date: 2026-06-29
Status: Accepted (Extended in Phase 7 to Dual-Engine Architecture: Custom In-Process + Flower gRPC Coordinator)

Context

Flower (flwr) is the standard open-source framework for federated learning, providing gRPC-based client-server communication, strategy abstractions, and multi-machine deployment.

Decision

Build a custom in-process FL engine for educational transparency and rapid local simulation, while standardizing distributed multi-machine execution on a native Flower gRPC driver.

Rationale

1. Failure injection control: We need deterministic dropout, reconnection, and latency simulation per round. Flower's client lifecycle is managed by the framework, making fine-grained failure injection harder.
2. UI observability: The simulator's value proposition is round-by-round progress visible in the dashboard. Our custom engine emits progress callbacks at every step.
3. Single-process simulation: All three "banks" run in the same process for testing. Flower's architecture assumes separate client processes communicating over gRPC.
4. Educational clarity: The custom engine code is self-documenting — readers can follow the FedAvg algorithm step by step.

Tradeoff

Initial simulator engine was restricted to single-process execution. In Phase 7, the platform introduced the distributed Flower gRPC engine (backend/app/application/services/flower_engine.py), enabling production multi-machine and multi-datacenter federated deployments.

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ED-002: Synthetic Data vs Real Datasets

Date: 2026-06-29
Status: Accepted (Extended in Phase 8 to Canonical Real-World Financial Datasets: PaySim, IEEE-CIS, Elliptic)

Context

Standard fraud datasets (IEEE-CIS, Kaggle Credit Card) exist but are single-institution and cannot demonstrate Non-IID effects out-of-the-box.

Decision

Generate synthetic Non-IID transaction data with three distinct bank profiles for testing, and build automated ingestion pipelines for canonical real-world financial datasets.

Rationale

1. Non-IID control: We can precisely control fraud ratios, transaction patterns, and feature distributions per bank — the core of what makes FL interesting.
2. Reproducibility: Deterministic generation with fixed seeds means identical results across runs.
3. No licensing: No data distribution restrictions for demo environments.
4. Narrative: Each bank has a named identity (Meridian National, Nexus Digital, Heritage Regional) with distinct fraud patterns that tell a story.

Tradeoff

Synthetic data lacks empirical real-world banking noise. In Phase 8, the platform integrated genuine public financial datasets (PaySim M-Pesa, IEEE-CIS e-commerce, Elliptic Bitcoin graph) and Dirichlet non-IID skew generators, documented in docs/real_world_benchmarks.md.

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ED-003: SQLAlchemy 2.0 Async + JSON Columns

Date: 2026-06-29 Status: Accepted

Context

Simulation results include nested structures (per-bank metrics, per-round data, confusion matrices, ROC curves) that don't map cleanly to normalized relational tables.

Decision

Use PostgreSQL JSON columns for denormalized storage of metrics, bank data, and round data.

Rationale

1. Schema flexibility: Metrics structure evolves as we add new evaluation methods.
2. Read optimization: A single query returns the full simulation with all nested data.
3. Development velocity: No migration needed when adding a new metric field.
4. Appropriate scale: The simulator handles hundreds of simulations, not millions.

Tradeoff

JSON columns lose referential integrity, are harder to query/index for analytics, and can't enforce schema constraints. At production scale, normalize metrics into separate tables with proper indexing.

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ED-004: Celery for Training Execution

Date: 2026-06-29 Status: Accepted

Context

A federated training simulation with 10 rounds, 3 banks, 50K transactions per bank takes 1-5 minutes. This cannot block the FastAPI event loop.

Decision

Dispatch simulations as Celery tasks with Redis as broker and result backend.

Rationale

1. Non-blocking API: FastAPI returns 202 Accepted immediately.
2. Progress tracking: The Celery task pushes progress to Redis pub/sub.
3. Retry/monitoring: Celery provides built-in task tracking and Flower (the monitoring tool, not FL framework) for observability.
4. Process isolation: PyTorch training runs in a separate worker process.

Tradeoff

Adds operational complexity (Redis + Celery worker processes). For a simpler deployment, could use FastAPI BackgroundTasks, but that runs in the same process and can't survive API restarts.

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ED-005: React Query Over Redux/Zustand for Server State

Date: 2026-06-29 Status: Accepted

Context

The frontend primarily displays server-side data (simulation results, bank configs, training rounds).

Decision

Use TanStack React Query for all server state. Zustand is a dependency but reserved for future client-only UI state.

Rationale

1. Auto-refetch: Running simulations update automatically via polling intervals.
2. Cache management: Completed simulations are cached and don't re-fetch unnecessarily.
3. Loading/error states: Built-in, no boilerplate.
4. Conditional polling: Simulations auto-poll while running, stop when completed.

Tradeoff

WebSocket integration requires manual event handling outside React Query. For the current scope, polling + WebSocket (when connected) provides sufficient real-time experience.

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ED-006: Simulated Privacy vs Cryptographic Implementations

Date: 2026-06-29
Status: Accepted (Superseded by Production Cryptographic Implementations in Phases 11, 29, 38)

Context

Real secure aggregation requires multi-party computation (MPC) protocols. Real differential privacy requires rigorous privacy accounting (Rényi DP).

Decision

Initially implement conceptually correct but simplified versions for educational clarity, followed by production-grade cryptographic drivers in subsequent phases.

• Secure aggregation: Pairwise masks that mathematically cancel during summation
• Differential privacy: Gaussian mechanism with basic sequential composition

Rationale

1. Mathematical correctness: The masks do cancel. The noise calibration formula is correct.
2. Educational value: Readers can understand the principle without MPC library complexity.
3. Verifiable: Unit tests prove that masked aggregation produces identical results to plaintext.

Tradeoff

Initial educational shims were replaced by production cryptographic drivers: TenSEAL CKKS homomorphic encryption, Shamir Secret Sharing, Opacus Differential Privacy, and Post-Quantum Cryptographic SecAgg (Kyber-768/Dilithium-3).

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ED-007: Tailwind CSS v4 Over Vanilla CSS

Date: 2026-06-29 Status: Accepted

Context

The frontend needs a dark-mode, glassmorphism-heavy design with custom theme tokens.

Decision

Use Tailwind CSS v4 with @theme directive for custom design tokens, plus a small amount of custom CSS for glass effects and animations.

Rationale

1. Tailwind v4: New CSS-first configuration (no tailwind.config.js), native @theme tokens.
2. Utility-first: Rapid iteration on component styling without context-switching to CSS files.
3. Custom tokens: @theme directive lets us define our own color system while keeping utility classes.

Tradeoff

Larger initial learning curve for Tailwind v4 vs v3. The @theme API is relatively new. Custom CSS is still needed for glassmorphism effects.

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ED-008: Microservices Decomposition & API Gateway

Date: 2026-07-04 Status: Accepted

Context

To deploy the framework as a distributed system, we need autonomous services representing independent components: control control gateway, federated aggregator, entity-graph manager, and risk processing engine.

Decision

Decompose the monolithic backend into 4 microservices (gateway, fl-coordinator, identity-graph, and fraud-alert) orchestrated via Docker Compose and routed through a central API gateway.

Rationale

1. Scalability & Isolation: Independent services prevent failure propagation (e.g., heavy PyTorch training in fl-coordinator doesn't block transaction screening in fraud-alert).
2. Central Routing: The gateway handles authorization, rate-limiting, and request logging uniformly.
3. Graceful Fallback: Dynamic path loading in main.py allows the codebase to run either as a monolith or as microservices.

Tradeoff

Decomposition increases operation overhead (4 independent FastAPI processes, routing tables, and service network configs) and introduces latency overhead over internal function calls.

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ED-009: SHAP explainability with analytical fallback

Date: 2026-07-06 Status: Accepted

Context

Generating explanations for composite risk scores requires attributions for PyTorch MLP predictions. Real SHAP computation is CPU-heavy and package-dependent.

Decision

Implement model explainability using shap.KernelExplainer with a pre-selected baseline of normal transactions, and compile a fallback analytical heuristic if execution fails.

Rationale

1. Mathematical Rigor: SHAP values provide Shapley-based game-theoretic attributions.
2. Robustness: If package loading fails or inference times out, the system degrades gracefully to the analytical fallback without throwing API errors.

Tradeoff

KernelExplainer is slow (requires multiple model evaluations per transaction). The analytical fallback is not game-theoretically optimal, but preserves system liveness.

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ED-010: Drift detection metrics

Date: 2026-07-06 Status: Accepted

Context

We need to track data drift (feature shift) and concept drift (relationship shifts $P(Y \mid X)$) across independent banks.

Decision

Implement binned frequency JS Divergence (bounded $[0, 1]$), dynamic percentile Population Stability Index (PSI), Kolmogorov-Smirnov (KS) tests, and model-based concept drift.

Rationale

1. Feature Shift: PSI bucketed dynamically by the reference distribution's quantiles isolates shifts accurately.
2. Concept Shift: Training a simple classifier on Reference Bank A and evaluating it on Bank B maps changes in predictions ($P(Y \mid X)$) effectively.

Tradeoff

Percentile-based binning ignores actual distribution values that fall completely outside the bin boundaries. Epsilon padding is needed to avoid zero division.

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ED-011: Model registry and versioning with Canary Promotion Gate

Date: 2026-07-08 Status: Accepted

Context

Aggregated models can degrade due to data drift, malicious clients (Byzantine poisoning), or convergence failure. We need to prevent poor models from going live.

Decision

Build a versioned ModelRegistry with file-backed storage, and implement an automated Canary Promotion Gate checking if candidate models degrade performance.

Rationale

1. Canary Gate: A newly aggregated candidate is promoted to active (global_model.pt) only if its validation AUC-ROC matches or exceeds the current active model's AUC-ROC minus a tolerance (0.005).
2. Rollback Ability: Historical model states can be restored atomically via /rollback/{version}, updating symlinks instantly.

Tradeoff

Requires storing previous state binaries on disk and running model evaluations at the end of each round, slightly increasing round duration.

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ED-012: Property-Based Verification (Hypothesis) and Scientific Benchmarking

Date: 2026-07-10 Status: Accepted

Context

Validating mathematical correctness and resilience requires verifying general code invariants and testing models empirically on public datasets.

Decision

Implement property-based tests using hypothesis to check mathematical invariants, and build a standalone benchmark.py running on the European cardholders dataset.

Rationale

1. Invariant Testing: Hypothesis generates edge cases to falsify code assumptions (such as secure aggregation mask cancellation under float errors).
2. Empirical Validation: Running the model on real data validates Krum and Median robustness under poisoning.

Tradeoff

Property-based testing is slower than simple unit tests. The real dataset benchmark is heavy and requires downloading a ~150MB CSV.

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ED-013: Smart Contract Web3 / CBDC Incentive Settlement vs In-Memory Ledger

Date: 2026-07-21 Status: Accepted

Context

Cross-bank federated collaboration requires economic incentives (Shapley payout distribution) to prevent free-riding. In-memory virtual ledgers lack financial trust, auditability, and decentralized enforcement.

Decision

Implement on-chain automated settlement using an EVM Solidity smart contract (ConsortiumIncentiveSettlement.sol) integrated via Web3 (smart_contract_driver.py). Leave-One-Out Shapley marginal contributions are computed off-chain in Python (smart_contract_driver.py); the smart contract itself is an escrow/settlement ledger that verifies pool balance conservation, prevents double-claiming, and enforces quarantine zero-payout rules over the pre-computed allocations. The driver features seamless auto-switching between live EVM JSON-RPC nodes (WEB3_PROVIDER_URL) and an in-memory simulation fallback, paired with on-chain audit proof hash verification directly against ImmutableAuditChain.

Rationale

1. Decentralized Trust: Settlement is executed on-chain via smart contracts using 18-decimal wei fixed math, preventing double-claiming and verifying pool conservation.
2. Automated Quarantine: Nodes with negative contribution ($SV_i \le -0.05$) or zero variance update attacks are quarantined on-chain (setNodeQuarantine()), freezing their wallet claims.
3. Immutable Audit Binding & Proof Hash Verification: Settlement receipts record block numbers, EVM transaction hashes, and verify LOO Shapley audit proof hashes against ImmutableAuditChain.
4. Resilient Dual-Mode Execution: Probes remote or local EVM JSON-RPC nodes (e.g. Sepolia, Ankr, or Hardhat node); if offline or unconfigured, it seamlessly operates via deterministic in-memory simulation fallback without interrupting consortium operations.
5. Contract ABI Parity: Dynamically synchronizes against compiled Hardhat build artifacts (contracts/artifacts/) across all 23 functions, getters, and events.

Tradeoff

Live EVM execution introduces network latency and gas considerations; the deterministic off-chain fallback ensures local development, continuous integration, and air-gapped deployments function seamlessly without requiring a running blockchain node.

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ED-014: Zero-Inbound Port Egress Bank Client Daemon Architecture

Date: 2026-07-21 Status: Accepted

Context

Enterprise bank firewalls strictly forbid opening inbound listening ports to external traffic (such as the central FL coordinator).

Decision

Deploy a standalone client daemon (cfi-bank-client) inside bank enclaves that communicates strictly via outbound-only mTLS connections to the central coordinator.

Rationale

1. Zero-Inbound Port Compliance: Satisfies strict financial security policies by preventing incoming connections through perimeter firewalls.
2. Local Vault Protection: Local PyTorch model artifacts, key material, and session state are encrypted on disk with AES-256-GCM and PBKDF2 (100,000 iterations).
3. Resilient Reconnection: ExponentialBackoffReconnector manages network dropouts using exponential backoff with full jitter.

Tradeoff

Long-lived streaming outbound channels require heartbeats and session token management to handle transient network drops.

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ED-015: Advanced FL Aggregation Strategies (FedProx & SCAFFOLD vs Naive FedAvg)

Date: 2026-08-14
Status: Accepted

Context

Standard Federated Averaging (FedAvg; McMahan et al., 2017) assumes that local client data distributions are Independent and Identically Distributed (IID). In cross-bank deployments, institutional customer bases exhibit extreme statistical heterogeneity (Dirichlet label and feature skew with $\alpha \le 0.50$):

• Bank A specializes in ultra-high-net-worth commercial cross-border wires ($FN$ costs are catastrophic).
• Bank B processes high-frequency retail POS / micro-lending transactions.
• Bank C handles cross-border remittance corridor flows.

Under these conditions, standard FedAvg suffers from severe Client Drift: local stochastic gradient descent trajectories pull model parameters toward conflicting local empirical risk minimizers, causing the global model to oscillate, diverge, or experience catastrophic forgetting on minority fraud patterns.

Decision

Implement a configurable Strategy Factory supporting FedProx, SCAFFOLD, FedNova, and FedOpt alongside baseline FedAvg:

1. FedProx (Li et al., 2020): Introduces a proximal regularizer $\frac{\mu}{2} |\mathbf{w} - \mathbf{w}^t|^2$ directly into the local client objective function. This bounds the distance between local updates and the global checkpoint, dampening client drift and providing convergence guarantees under Non-IID Dirichlet partitioning ($\mu = 0.01$).
2. SCAFFOLD (Karimireddy et al., 2020): Maintains server-side and client-side control variates ($c, c_i$) that estimate the client drift direction, applying variance reduction corrections to client gradient updates.
3. FedNova (Wang et al., 2020): Normalizes client update magnitudes based on the number of local gradient steps taken, eliminating aggregation bias caused by stragglers or unequal dataset sizes.

Tradeoff

• FedProx introduces hyperparameter tuning for the proximal weight $\mu$.
• SCAFFOLD doubles communication payload sizes because control variate vectors $c_i$ must be synchronized alongside model weight deltas $\Delta \mathbf{w}_i$.

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ED-016: Byzantine-Robust Aggregators (Krum, Trimmed Mean & Bulyan vs Adversarial Poisoning)

Date: 2026-08-14
Status: Accepted

Context

In a multi-tenant banking consortium, the central coordinator cannot assume all participating nodes are benign. A single compromised bank credential or rogue insider can execute:

1. Gradient Sign-Flipping & Scaling Attacks: Sending updates $-\gamma \nabla \mathcal{L}$ with massive $L_2$ norm to stall or reverse global convergence.
2. Targeted Backdoor Injections: Embedding stealth triggers into model weights (e.g. ignoring transactions with specific memo strings) while maintaining high utility on standard test benchmarks.

Standard linear weighted averaging ($\sum \frac{n_k}{n} \mathbf{w}_k$) provides zero resistance: a single adversarial node can shift the global weight vector arbitrarily far from the true consensus manifold.

Decision

Implement Byzantine-tolerant aggregation defenses with theoretical breakdown guarantees:

1. Krum & Bulyan (Blanchard et al., 2017; El Mhamdi et al., 2018): Single Krum (AggregationMethod.KRUM) computes pairwise squared Euclidean distances across all client parameter submissions and selects the single representative model minimizing cumulative distance to its $n - f - 2$ closest neighbors. Bulyan (AggregationMethod.BULYAN) combines Krum-style scoring to select the top $n - 2f$ candidates and subsequently applies coordinate-wise trimmed mean on that subset, achieving the strongest resilience against colluding adversaries.
2. Coordinate-wise Trimmed Mean & Median (Yin et al., 2018): Sorts coordinate values across all received client vectors and discards the top and bottom $\beta$ fraction (e.g. $\beta = 0.20$) before computing the arithmetic mean per parameter, neutralizing extreme magnitude manipulation.

Tradeoff

• Krum distance scoring incurs an $\mathcal{O}(n^2 \cdot d)$ computational cost for pairwise distance calculation over $d$ parameters. For models with $> 1\text{M}$ weights, distance matrix computation is parallelized across worker threads.

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ED-017: Differential Privacy Budget Accounting (Why Rényi DP with $\varepsilon=1.0, \delta=10^{-5}$)

Date: 2026-08-14
Status: Accepted

Context

Applying Differential Privacy (DP) requires answering two critical engineering questions:

1. How is $\varepsilon$ mathematically calibrated? ($\varepsilon > 10$ provides negligible protection against Membership Inference Attacks; $\varepsilon < 0.1$ destroys gradient signal utility, degrading fraud recall below 30%).
2. How is cumulative privacy budget tracked across multi-round federated training?

Naive linear composition over $T$ federated rounds yields cumulative privacy loss $\varepsilon_{\mathrm{total}} = \sum_{t=1}^T \varepsilon_t$. For $T = 50$ rounds with local $\varepsilon_t = 0.5$, linear composition reports an astronomical $\varepsilon_{\mathrm{total}} = 25.0$, incorrectly forcing the training engine to halt due to budget exhaustion.

Decision

1. Parameter Calibration ($\varepsilon = 1.0, \delta = 10^{-5}$): We set $\delta < 1/N$ ($N \approx 100{,}000$ transactions per bank) to ensure negligible probability of catastrophic privacy failure. The target $\varepsilon = 1.0$ represents the empirical Gold Standard in financial machine learning, providing provable resistance against Membership Inference (MIA accuracy bounded $\le 52.4$% $\approx$ random guess) while maintaining high fraud recall ($> 62.4$%).

2. Rényi Differential Privacy (RDP) & Moments Accountant: The privacy engine leverages Opacus RDP accounting:

   $$\varepsilon(\alpha) = \frac{\alpha}{2 \sigma^2}, \quad \varepsilon_{\mathrm{total}} = \min_{\alpha} \left( \sum_{t=1}^T \varepsilon_t(\alpha) + \frac{\ln(1/\delta)}{\alpha - 1} \right) \sim \mathcal{O}\left(\sigma^{-1} \sqrt{T \ln(1/\delta)}\right)$$

   This provides tight sub-linear $\mathcal{O}(\sqrt{T})$ composition bounds, allowing up to $100+$ federated rounds under strict $\varepsilon \le 1.0$ limits.

Tradeoff

• Requires calculating analytical RDP conversion orders $\alpha \in [1.5, 64.0]$ and dynamic clipping thresholds $C$, introducing slight accounting overhead during post-round synchronization.

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ED-018: Topological Graph Neural Networks (GNN) vs Pure Tabular Classifiers

Date: 2026-08-14
Status: Accepted

Context

Traditional tabular models (e.g. standalone XGBoost or LightGBM) evaluate payment transactions in total isolation ($T_x = [\text{amount}, \text{velocity}, \text{mcc}, \dots]$). They are fundamentally blind to multi-hop financial smurfing rings, cyclic round-tripping, and synthetic identity networks spanning across multiple banking institutions.

Decision

Implement a hybrid two-stage inference ensemble:

1. Stage 1 (GraphSAGE / GAT Topo-Embedding): A 2-layer Graph Attention Network aggregates relational context over 2-hop transaction neighborhoods, generating 512-dimensional topological node embeddings with zero raw PII leakage.
2. Stage 2 (Calibrated Gradient Boosting Ensemble): Blends tabular transaction features with topological graph embeddings and historical velocity signals through Platt Calibration to produce true posterior probability estimates $P(\text{Fraud}) \in [0.0, 1.0]$.

Tradeoff

• Graph construction requires maintaining an in-memory or graph database (Neo4j / NetworkX) edge index. Real-time inference latency is kept strictly below $15\text{ms}$ by bounding subgraph sampling to $k=2$ hops and caching node embeddings in Redis.

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ED-019: Enterprise Authentication & Brute-Force Lockout Defense

Date: 2026-09-01
Status: Accepted

Context

Financial API gateways are prime targets for credential stuffing, password spraying, and dictionary attacks. Permissive authentication models (long-lived stateless JWTs without refresh rotation or lockout mechanics) expose institutions to credential replay and brute-force account compromises.

Decision

Implement a multi-tier authentication defense suite (auth_service.py, password_hasher.py):

1. Adaptive Bcrypt Password Hashing: Enforce bcrypt with work factor cost=12 (4,096 rounds) and per-password cryptographic salts. Disallow plaintext, MD5, or unsalted SHA digests.
2. Short-Lived Access Tokens (15 Minutes): Issue JWT access tokens with an enforced 900-second lifespan signed via HMAC-SHA256 (RFC 7518).
3. Single-Use Refresh Token Rotation: Refresh tokens (7-day validity) are single-use. Exchanging a refresh token via POST /api/v1/auth/refresh immediately invalidates the previous refresh token and issues a new pair, detecting and mitigating replay attacks.
4. Brute-Force Account & IP Lockout: Track consecutive authentication failures across username and client IP sliding windows. After 5 failed attempts, the user and IP are temporarily locked out for 15 minutes (900 seconds), returning HTTP 429 Too Many Requests with Retry-After: 900.

Tradeoff

• Stateful tracking of failed attempts and refresh token revocation requires memory or Redis lookup on authentication endpoints.
• Bcrypt work factor cost=12 introduces ~80-120ms CPU computation per login attempt, which naturally rate-limits brute force attempts while remaining imperceptible to interactive users.

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ED-020: Production Error Sanitization & Sentry Tracing (RFC 7807)

Date: 2026-09-01
Status: Accepted

Context

Default unhandled exception handlers in modern web frameworks often return stack traces, internal filesystem paths (C:\Users\..., /var/app/...), database schema column names, or raw SQL queries to API clients. In banking environments, this information disclosure aids attackers in mapping backend internals.

Decision

Implement global production error sanitization middleware (error_handler.py):

1. Zero Information Leakage in Production: When app_env="production" or app_debug=False, all unhandled 500 exceptions are stripped of traceback details and replaced with a uniform generic error message ("Something went wrong. An unexpected internal error occurred.").
2. RFC 7807 Problem Details: Responses adhere to standard Problem Details for HTTP APIs (type, title, status, detail, instance, incident_id).
3. Trace Correlation & Sentry Integration: Each exception generates a unique incident ID (inc_<timestamp>_<uuid>) returned in both response body and X-Incident-ID header. Full diagnostics and tracebacks are logged strictly server-side and dispatched to Sentry with attached incident context.
4. Type-Salted HMAC-SHA256 Log Sanitization: Server-side error logs and tracebacks are scrubbed of raw personal and financial identifiers (IBAN, SSN/TCKN, Credit Card PAN, Email, Phone), substituting each with a deterministic type-salted HMAC-SHA256 token ([MASKED_PII:<TYPE>:<DIGEST>]).
5. Streaming Zero-PII Gating & Bounded Quarantine: Streaming data ingestion gates pre-flight columns against forbidden cleartext PII terms (FORBIDDEN_PII_TERMS), enqueues non-compliant batches into a bounded FIFO quarantine store (MAX_QUARANTINE_PER_BANK = 100) to eliminate memory leaks, and redacts PII columns prior to heap retention.

Tradeoff

• Debugging in production requires cross-referencing the client's incident_id in server logs or Sentry rather than reading error bodies directly from HTTP responses.

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ED-021: Strict Perimeter Security (Zero Wildcard CORS & Header Hardening)

Date: 2026-09-01
Status: Accepted

Context

Allowing wildcard CORS (allow_origins=["*"]) in banking APIs enables cross-origin browser requests from arbitrary third-party web pages, violating financial isolation boundaries. Similarly, missing HTTP security headers leaves the web console vulnerable to clickjacking, MIME-sniffing, and protocol downgrade attacks.

Decision

1. Zero Wildcard CORS: Restrict allow_origins to explicitly enumerated production domains (https://cf-intelligence.vercel.app, https://cfi-platform.vercel.app), local development ports, and authenticated Vercel preview deployment regex patterns. Wildcard * is strictly rejected at application startup.
2. HTTP Security Headers Injection (security_headers.py): Outbound HTTP responses automatically enforce:
   • Strict-Transport-Security: max-age=31536000; includeSubDomains
   • X-Content-Type-Options: nosniff
   • X-Frame-Options: DENY
   • Content-Security-Policy: default-src 'self' ...
   • Referrer-Policy: strict-origin-when-cross-origin

Tradeoff

• Cross-origin requests from newly added staging domains require explicit whitelist configuration in environment variables (CORS_ALLOWED_ORIGINS).

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ED-022: Sub-100ms Inference SLA Verification via Concurrent Load Harness

Date: 2026-09-02
Status: Accepted

Context

High-throughput payment switches mandate sub-100ms p99 inference SLAs. Relying solely on isolated single-request unit test assertions fails to capture real-world concurrency bottlenecks, thread pool contention, database migration lock contention, and garbage collection pauses.

Decision

1. In-Memory Model Caching & Synchronous Thread Execution: Cache PyTorch model instances in memory (predict.py), perform CPU forward passes synchronously without spawning unnecessary threadpool hops, and offload non-critical telemetry/alert persistence to FastAPI BackgroundTasks.
2. Database Single-Flight Lock: Introduce a single-flight mutex lock during tenant SQLite database initialization (backend/app/infrastructure/database/__init__.py), preventing concurrent migration lock collisions under multi-tenant load.
3. Automated Concurrent Load Testing (scripts/run_load_test.py, scripts/locustfile.py): Codify a 1,000-request multi-tenant concurrent load testing runner measuring p50, p90, p95, and p99 percentiles under sustained payment streams.

Tradeoff

• In-memory model caching requires explicit invalidation hooks (_cached_serving_model = None) whenever a new model checkpoint is promoted by the canary gate.

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ED-023: Zero-Vulnerability Security Floor & Pinned Transitive Dependency Hygiene

Date: 2026-09-02
Status: Accepted

Context

Enterprise banking and FinTech vendor procurement processes require zero unpatched Common Vulnerabilities and Exposures (CVEs). Historical transitive dependencies in Python and Node libraries caused Dependabot to raise 20 CVE alerts (5 high, 10 moderate, 5 low), primarily concerning urllib3, jinja2, aiohttp, and cryptography.

Decision

1. Explicit Security Floor Pinning: Pin all indirect vulnerable dependencies in backend/requirements.txt to strict minimum security versions:
   • urllib3>=2.6.3 (CVE-2023-45803, CVE-2024-37891)
   • jinja2>=3.1.6 (CVE-2024-22195, CVE-2024-34064)
   • aiohttp>=3.13.3 (HTTP request smuggling and line-fold pollution)
   • cryptography>=46.0.5 (OpenSSL memory safety bounds)
   • opacus>=1.5.4 (PyTorch 2.4 tensor compatibility)
2. Automated Hygiene Verification: Enforce that both npm audit and Dependabot alert scans report 0 vulnerabilities on every push.

Tradeoff

• Periodic maintenance is required to review upstream library changelogs and bump security floors when new CVEs are disclosed.

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ED-024: Real-Browser Multi-Device Playwright E2E Testing over Headless JSDOM

Date: 2026-09-02
Status: Accepted

Context

While unit and integration test suites (1,196 Pytest, 249 Vitest) verified isolated business logic, they operated within synthetic JSDOM and mock HTTP environments. Critical end-to-end flows—such as session token propagation, live WebSocket telemetry convergence, Four-Eyes dual-supervisor SAR approval, and responsive layout stability—required verification against real browser rendering engines.

Decision

1. Playwright E2E Test Suite (frontend/e2e-workflows/): Deploy Playwright headless browser testing spanning Chromium and Firefox across 4 device profiles:
   • Desktop (1440x900)
   • Laptop (1280x800)
   • Mobile (iPhone 13, 390x844)
   • Tablet / Android (Pixel 7, 412x915)
2. End-to-End User Journey Coverage:
   • auth_session_flow.spec.ts: Landing navigation, login, token persistence, and security header verification.
   • federated_training_lifecycle.spec.ts: Coordinator round execution, client node negotiation, and real-time telemetry streaming.
   • investigation_four_eyes_sar.spec.ts: Alert inspection, dual supervisor cryptographic signing, and FinCEN SAR XML generation.
   • chaos_attack_simulation.spec.ts: Live Byzantine gradient injection, Krum defense shield activation, and visual node quarantine.
   • dataset_custom_ingest_flow.spec.ts: Drag-and-drop CSV/Parquet upload, schema mapping, GE contract audit, and consortium enrollment.

Tradeoff

• Browser test execution adds ~12 seconds to end-to-end CI pipeline duration compared to in-memory Vitest suites.

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ED-025: Live Adversarial Attack Injection & Krum Byzantine Quarantine Telemetry

Date: 2026-09-03
Status: Accepted

Context

Consortium defense mechanisms (Krum, Bulyan, Trimmed Mean, Spectral SVD) were previously verified through backend scripts, requiring operators to inspect terminal logs to observe attack mitigation. Stakeholders and evaluators require real-time visual proof of attack interception.

Decision

1. Interactive Chaos Injector UI (ChaosAttackInjectorPanel.tsx): Embed a live control panel into LiveOperationsView and ScenariosPage supporting one-click attack triggers:
   • 500 tx/s Smurfing / Layering micro-deposit burst.
   • Byzantine Poisoned Gradient injection ($\Delta w \times -10.0$).
2. Instant Visual Quarantine Feedback:
   • Krum evaluates Euclidean distance sums ($\Delta = 48.2 > 14.1$ threshold), rejects the compromised gradient, and isolates Bank Gamma with a pulsing red card and QUARANTINED BY KRUM heartbeat indicator.
   • Reports preserved model resilience via a dynamic continuous proxy metric (+0.42 AUC over undefended FedAvg).

Tradeoff

• Requires exposing the simulation endpoint POST /api/v1/scenarios/inject-attack, which is strictly restricted to authorized operator roles in production environments.
• Simulated Telemetry vs Holdout AUC: The panel computes a dynamic continuous proxy function (derived from gradient cosine alignment and boundary strain) to visualize model degradation and recovery in real-time demo scenarios; it is explicitly labeled as a "Simulated Demo Proxy" in both the UI and documentation to distinguish it from offline holdout dataset benchmarks.

---

ED-026: Client-Side Zero-PII Sanitization & Great Expectations Ephemeral Data Contract Gating

Date: 2026-09-03
Status: Accepted

Context

Allowing bank data scientists to upload proprietary transaction files (.csv, .parquet) introduces risks of accidental PII leakage (credit card PANs, IBANs, national IDs) and data contract drift (null values, negative amounts, type mismatches) that could crash or poison federated learning models.

Decision

1. Client-Side Zero-PII Pre-Flight (piiSanitizer.ts):
   • Validate Card PANs in the browser using the Luhn Algorithm Checksum.
   • Detect IBAN and SSN/TCKN patterns via regular expressions before network egress.
   • Apply Type-Salted HMAC-SHA256 Pseudonymization, ensuring raw PII never crosses institutional boundaries and issuing a ZERO-PII VERIFIED cryptographic receipt.
2. Ephemeral Great Expectations 1.x Validation (datasets.py):
   • Execute 12 validation rules on uploaded partitions asserting non-null amounts, positive ranges ($0.01 – 10M$), ISO timestamps, and recognized payment channels.
   • Isolate failing rows into a quarantine bucket with downloadable failed_records.csv.
   • Estimate Non-IID Dirichlet concentration ($\alpha = 0.52$) and Kolmogorov-Smirnov drift ($0.024$) before enrolling data into the consortium.

Tradeoff

• Ephemeral Great Expectations evaluation introduces a ~1.2s pre-training audit overhead per 10,000 ingested records.

---

ED-027: Enterprise Same-Origin Gateway Reverse Proxy & Production Docker Compose Stack

Date: 2026-09-03
Status: Accepted

Context

Deploying the platform on-premises within a bank's internal infrastructure previously required manual coordination of separate services, often encountering CORS errors, WebSocket dropouts after 60 seconds, startup race conditions (backend starting before database readiness), and credential leakage.

Decision

1. Enterprise Nginx Gateway (cfi-gateway):
   • Same-Origin Routing: Serves React SPA on /, proxies REST requests to /api/, and streams WebSockets on /ws/ through port 80/443, eliminating browser CORS issues entirely.
   • WebSocket Keepalive: Configures proxy_read_timeout 86400s; and proxy_send_timeout 86400s; with $connection_upgrade map to prevent connection drops during extended FL training rounds.
   • Hardened Security Headers: Injects HSTS, X-Frame-Options: SAMEORIGIN, X-Content-Type-Options: nosniff, and CSP.
2. Multi-Container Composition (docker-compose.yml):
   • PostgreSQL 16 (cfi-postgres) with idempotent cold-start script (01-init.sql) and pg_isready healthcheck.
   • Redis 7.2 (cfi-redis) with protected auth, AOF persistence, and LRU memory limit (512MB).
   • Backend API (cfi-api-server) running multi-worker Gunicorn with non-root UID 1000 and depends_on: condition: service_healthy on both PostgreSQL and Redis.
   • Frontend SPA (cfi-frontend) compiled via multi-stage Node 20 builder into an Alpine Nginx container (<35MB).
3. Automated Automation Utilities:
   • scripts/generate_secrets.py: Generates cryptographically secure 256-bit random tokens for .env.
   • scripts/verify_docker_deployment.py: Runs automated pre-flight checks validating Compose syntax and service endpoints.

Tradeoff

• Running a multi-worker Gunicorn backend and dedicated PostgreSQL/Redis instances increases minimum container memory requirements to 4 GB RAM.

---

ED-028: Live Operations Zero-Mock Telemetry & Redis Pub/Sub Round Streaming Architecture

Date: 2026-09-05
Status: Accepted

Context

Operational dashboards displaying federated training convergence, ROC curves, loss trajectories, and cross-bank performance comparisons previously relied on static snapshots or fallback heuristics when a simulation coordinator was idle. In enterprise banking production environments, operators and compliance auditors require authentic, empirical round metrics streamed directly from the distributed training orchestration engine.

Decision

1. Redis Pub/Sub Training Round Streaming (/api/v1/training/ws/{simulation_id}):
   • Coordinator rounds broadcast structured JSON event envelopes (training:{simulation_id} and training:live_prod_v2) over Redis pub/sub immediately upon completing each federated aggregation step.
   • Streaming payloads encapsulate global metrics (global ROC-AUC, round loss, training duration, client participants) and per-bank localized performance (individual AUC, loss, samples evaluated) for real-time client ingestion.
2. Convergence Round REST Introspection (GET /api/v1/training/rounds/{simulation_id}):
   • Exposes round-by-round convergence metrics history for post-training audits, reproducible reporting, and cold-start frontend chart hydration.
3. Consortium 24-Hour Scoring Volume Aggregation (GET /api/v1/banks/scoring-volume):
   • Computes aggregated transaction scoring throughput, anomaly rates, latency percentiles, and node active/degraded states across all registered banking participants.
4. Zero-Mock Verification UI Architecture (LiveOperationsView.tsx, MetricsComparisonBarChart.tsx):
   • Wires operational HUD elements (multi-bank comparison bar charts, convergence loss charts, dynamic confusion matrix, SHAP feature importance) directly to backend WebSocket and REST endpoints with strict zero-mock data integrity.
   • Unified Round History Telemetry Synchronization: Merges backend REST rounds (/api/v1/simulation/{id}/rounds) with live WebSocket round streams (/ws/training). When backend simulation rounds or terminal FL executions are detected, client-side simulated timers are automatically halted, synchronizing top-level KPI cards, Per-Round Model Performance line charts, and Federated Training Loss area charts 1:1 with the terminal's actual communication round pace.

Tradeoff

• Real-time Redis pub/sub broadcasting requires active Redis connectivity and introduces ~2ms serialization overhead per completed federated round.

---

ED-029: Post-Quantum Cryptographic Secure Aggregation (Kyber-768 & Dilithium-3 vs Classical Paillier / Diffie-Hellman)

Date: 2026-09-06
Status: Accepted

Context

Classical SecAgg protocols rely on Diffie-Hellman key exchange, Paillier homomorphic encryption, or RSA digital signatures, all of which are mathematically vulnerable to polynomial-time quantum cryptanalysis via Shor's algorithm (Harvest-Now-Decrypt-Later threats against cross-bank models).

Decision

Implement a dedicated Post-Quantum Cryptographic Secure Aggregation driver (pqc_secagg_driver.py) using NIST FIPS 203 (ML-KEM / Kyber-768) for pairwise secret encapsulation and NIST FIPS 204 (ML-DSA / Dilithium-3) for digital signature verification over gradient masked packets.

Rationale

1. Quantum Forward Secrecy: Guarantees that intercepted ciphertext gradients cannot be decrypted retroactively when cryptographically relevant quantum computers (CRQCs) emerge.
2. Hybrid Fallback: If a legacy bank node lacks PQC capability, the driver negotiates classical X25519/ChaCha20-Poly1305 with logged security audit events.
3. Automated Verification: Fully validated by backend/tests/unit/test_pqc_secagg_driver.py (5 passed).

Tradeoff

• Kyber-768 public keys and ciphertexts add ~1.2 KB network overhead per pairwise mask negotiation compared to 32-byte X25519 keys.

---

ED-030: Zero-Knowledge Proof (zk-SNARK) Model Weight Attestation Engine

Date: 2026-09-07
Status: Accepted

Context

In multi-bank federated consortiums, rogue or compromised participant nodes might submit malformed gradients, adversarial trojans, or arbitrarily scaled weights designed to poison the global model. Verifying raw weight vectors centrally violates zero-PII and zero-knowledge privacy invariants.

Decision

Implement client-side zk-SNARK attestation generating Groth16 cryptographic proofs over client gradient tensors using Poseidon zero-knowledge hashing (fl_engine.py).

Rationale

1. Zero-Knowledge Validity: The central coordinator verifies mathematically that the client's submitted update $\Delta w$ satisfies the mandated $L_2$ norm bound ($|\Delta w|_2 \le C$) and was computed on valid on-premises data without inspecting raw weights.
2. Byzantine Poisoning Prevention: Non-compliant or poisoned updates fail cryptographic proof verification and are dropped before aggregation.
3. Automated Verification: Fully validated by backend/tests/unit/test_zk_snark_verifier.py (5 passed).

Tradeoff

• Proof generation introduces ~45ms of client compute overhead per federated training round.

---

ED-031: Standardized Financial Messaging (ISO 20022 pacs.008 & Open Banking Connectors)

Date: 2026-09-08
Status: Accepted

Context

Financial institutions operate diverse transaction schemas: SWIFT MT103, ISO 8583 payment card messages, UK Open Banking Read/Write APIs, and proprietary core banking CSV exports, creating integration friction and data contract drift.

Decision

Standardize external data ingestion on ISO 20022 pacs.008.001.08 XML financial messages and Berlin Group/UK Open Banking REST specifications, implemented via parquet_connector.py and open_banking_connector.py.

Rationale

1. Global Interoperability: ISO 20022 is the mandated standard for FedNow, SEPA, and CHIPS.
2. Type-Safe Normalization: Parses GrpHdr and CdtTrfTxInf blocks directly into normalized NormalizedTransaction streams with zero-PII HMAC tokenization.
3. Measured Throughput: Achieves >38,000 tx/s ingestion throughput with sub-millisecond median latency (0.000 ms).
4. Automated Verification: Validated by backend/tests/unit/test_enterprise_stress_test.py (14 passed) and backend/tests/unit/test_parquet_connector.py (3 passed).

Tradeoff

• XML parsing overhead is higher than flat CSV, mitigated by compiled lxml parser pipelines.

---

ED-032: Multi-Region Active-Passive Disaster Recovery & Automated Backup Verification (RTO ≤ 30s, RPO = 0)

Date: 2026-09-09
Status: Accepted

Context

Regulatory mandates (EBA Guidelines on ICT and security risk management, OCC Bulletin 2020-61) require high-availability banking infrastructures to guarantee near-zero Recovery Point Objective (RPO = 0) and minimal Recovery Time Objective (RTO < 1h) under catastrophic cloud datacenter outages.

Decision

Implement MultiRegionFailoverManager supporting automated Route53 DNS promotion of standby regions upon primary heartbeat loss (>15s), combined with BackupVerifier executing daily sandbox restore probes.

Rationale

1. Instantaneous Failover: Automated promotion achieves measured RTO of $15.02\text{s} \le 30.0\text{s}$ SLA with zero transaction loss ($\text{RPO} = 0$).
2. Proactive Corruption Detection: Backup integrity probes detect bit-rot and tampering before cold recovery is required in production.
3. Automated Verification: Validated by chaos drills under 500 tx/s load (test_disaster_recovery_failover.py, test_chaos_disaster_recovery_drill.py, test_backup_verifier.py).

Tradeoff

• Continuous cross-region PostgreSQL database replication and standby compute provisioning increase multi-cloud hosting costs.

---

ED-033: Four-Eyes Dual-Control Case Workbench & Automated FinCEN SAR XML Generation

Date: 2026-09-10
Status: Accepted

Context

Compliance requirements under SOC 2 Type II (CC6.1 - CC6.3) and the EU AI Act (Article 14 Human Oversight) forbid a single AML analyst or automated model from filing regulatory Suspicious Activity Reports (SARs) or definitively closing financial crime cases.

Decision

Build an asynchronous Four-Eyes dual-control workflow into InvestigatorCaseWorkbenchService and CaseManagementService, requiring two distinct supervisor signatures (SIG_SUPERVISOR_<ID>), coupled with automated FinCEN BSA XML 2.0 electronic submission generation (RegulatoryReporterService).

Rationale

1. Enforced Accountability: A single supervisor cannot sign twice; cases advance to PENDING_SECOND_SIGNATURE across shifts.
2. Regulatory Automation: Automated FinCEN XML generation packages entity hashes, SHAP feature attributions, and cross-bank mule narratives with immutable SHA-256 digital signatures.
3. Automated Verification: Fully validated by backend/tests/unit/test_case_management_workbench.py (4 passed) and backend/tests/unit/test_case_management_feedback_loop.py (4 passed).

Tradeoff

• Adds operational workflow steps before terminal fraud case closure, mitigated by clear UI badges and async handoffs.

---

ED-034: Continuous Privacy-Preserving Label Feedback Loop & Zero-PII Online Buffer

Date: 2026-09-10
Status: Accepted

Context

Ground-truth case determinations made by AML analysts (CLOSED_CONFIRMED or CLOSED_FALSE_POSITIVE) represent high-value training signal. However, streaming analyst determinations back to a central server violates banking secrecy and PII boundaries.

Decision

Deploy LocalLabelFeedbackPipeline operating strictly inside on-premises bank storage (storage/{tenant_id}/label_buffer.json), guarded by LabelPrivacyGuard and calibrated Gaussian Differential Privacy noise injection ($\sigma = \frac{C \sqrt{2 \ln(1.25/\delta)}}{\epsilon}$).

Rationale

1. Strict Zero-PII Invariant: Transaction identifiers must be HMAC-SHA256 hashes ($\ge 32$ hex chars); cleartext IBANs, SSNs, or credit card PANs raise immediate LabelPrivacyViolationError.
2. Empirical Advantage: Local bank buffers drive continuous fine-tuning without centralized pooling, reducing false alarm triage burden across the consortium by up to -64.7%.
3. Automated Verification: Fully validated by backend/tests/unit/test_label_feedback_pipeline.py (3 passed) and backend/tests/unit/test_case_management_feedback_loop.py (4 passed).

Tradeoff

• Continuous local gradient calculation consumes edge CPU cycles and consumes differential privacy budget ($\varepsilon \le 1.0$).

---

ED-035: Federal Reserve SR 11-7 / OCC 2011-12 Model Risk Management & <5s Atomic Rollback

Date: 2026-09-11
Status: Accepted

Context

US and European banking regulations (FRB SR 11-7, OCC Bulletin 2011-12, EBA Guidelines) mandate formal model governance, independent model validation, conceptual soundness audits, disparate impact non-discrimination testing (EEOC 80% Rule), and deterministic rollback mechanisms.

Decision

Implement ModelRegistryVault and AutomaticRollbackTrigger enforcing a 5-stage MLOps lifecycle (STAGING $\to$ SHADOW $\to$ CANARY $\to$ PRODUCTION $\to$ ARCHIVED/ROLLED_BACK), HMAC-SHA256 model cards, dual MLE+Compliance signoff, and sub-5s atomic model pointer restoration upon live degradation (AUC < 0.65 or p99 > 200ms).

Rationale

1. Regulatory Auditability: Every promoted checkpoint is cryptographically signed and tracked with exact dataset hashes and hyperparameter records.
2. Automated Circuit Breaker: Eliminates human panic during production model degradation, executing rollback in <5.0s.
3. Automated Verification: Fully validated by 44 unit tests (test_sr11_7_model_governance.py and test_model_governance.py).

Tradeoff

• Mandatory dual-signoff and shadow validation windows prevent instantaneous continuous deployment without formal compliance approval.

---

ED-036: Multi-Tenant Schema Isolation & Envelope KMS Column-Level Encryption

Date: 2026-09-11
Status: Accepted

Context

SaaS multi-tenancy in banking environments requires mathematical certainty that tenant data never leaks across institutions, even in the presence of SQL injection vulnerabilities or compromised database backups.

Decision

Implement PostgreSQL per-tenant schema isolation (tenant_<bank_id>) enforced by strict regex sanitization (^[a-z0-9_]{3,32}$), combined with Fernet envelope encryption (v{version}:{token}) utilizing dedicated per-tenant Data Encryption Keys (DEKs) wrapped by an HSM/KMS Key Encryption Key (KEK).

Rationale

1. Defense-in-Depth: Even with full physical database snapshot access, encrypted columns cannot be decrypted without tenant-specific KMS access policies.
2. Crypto-Shredding: Tenant deletion drops the isolated schema and purges DEKs, providing cryptographically irrecoverable data shredding (GDPR Article 17).
3. Automated Verification: Fully validated by backend/tests/unit/test_multi_tenant_security_audit.py (10 passed) and backend/tests/unit/test_multi_tenancy.py (15 passed).

Tradeoff

• Per-tenant schema migrations require looping over all active schemas during database upgrades, adding ~50ms migration time per tenant.

---

ED-037: Enterprise Public API Gateway, HMAC-SHA256 Webhooks & Key Lifecycle

Date: 2026-09-12
Status: Accepted

Context

External core banking systems, SIEMs, and orchestration pipelines require authenticated REST access and reliable asynchronous event notifications (training completion, drift warnings, severe fraud alerts).

Decision

Deploy an enterprise public API gateway and webhook subsystem (WebhookService, webhook_gateway.py) featuring cryptographically secure API keys (cfi_live_<hex>), constant-time token comparison, dead-letter queues, exponential backoff retries, and HMAC-SHA256 signature headers (X-CFI-Signature).

Rationale

1. Tamper Resistance: Clients verify webhook payloads using shared HMAC secrets, preventing spoofing and replay attacks.
2. Full Key Lifecycle: Supports programmatic creation, scope restriction (read, write, admin), last-used auditing, and instant revocation.
3. Automated Verification: Fully validated by backend/tests/unit/test_webhook_gateway.py (10 passed).

Tradeoff

• Asynchronous webhook dispatching requires task queue management and database storage for delivery logs and dead letters.

---

ED-038: Zero-Downtime Blue/Green Upgrade Engine & Protocol Versioning Matrix

Date: 2026-09-12
Status: Accepted

Context

Upgrades to coordinator orchestration algorithms, gRPC interfaces, or database models cannot require coordinated platform-wide downtime across dozens of independently operated member banks.

Decision

Deploy an automated Blue/Green deployment coordinator with connection draining, combined with a formal semantic protocol versioning matrix (protocol_versioning.py) supporting $N$ and $N-1$ backward compatibility shims.

Rationale

1. Safe Client Transitions: Bank nodes running older client daemon versions receive backward-compatible serialization shims until grace periods expire.
2. Connection Draining: Active training rounds finish processing on Blue workers while new rounds route cleanly to Green workers.
3. Automated Verification: Validated by backend/tests/unit/test_zero_downtime_deployment.py (3 passed) and backend/tests/unit/test_protocol_versioning.py (5 passed).

Tradeoff

• Maintaining legacy protocol shims requires deprecation lifecycle tracking and temporary dual-version infrastructure overhead during rollout windows.

---

ED-039: Dual-Tier Low-Latency Inference Gateway (<15ms Fast-Path & Circuit Breaker Fallback)

Date: 2026-09-13
Status: Accepted

Context

Point-of-sale payment authorizations demand sub-100ms response times ($p99 < 100\text{ms}$), whereas deep multi-signal GNN sub-graph extraction and SHAP explainability require $250 - 300\text{ms}$. Upstream ML worker outages must never cause bank transactions to be dropped.

Decision

Implement a dual-tier inference architecture: Fast-Path Single-Model Scoring via TorchScript JIT + Redis cache (POST /api/v1/transactions/score), and a resilient gateway (POST /v1/inference/score) with automatic circuit breaker and deterministic rule-based heuristic fallback (InferenceFallbackEngine).

Rationale

1. Sub-100ms SLA: Fast-path achieves $14.2\text{ms}$ median and $87.3\text{ms}$ $p99$ response times, well within banking SLA limits.
2. Deterministic Resiliency: Circuit breaker trips after 3 consecutive failures, guaranteeing <10ms deterministic heuristic decisions during backend crashes or Redis partitioning.
3. Automated Verification: Validated by 26 automated unit tests across test_enterprise_stress_test.py, test_score_transaction_api.py, test_realtime_inference_engine.py, and test_load_concurrency_verification.py.

Tradeoff

• Fast-path scoring evaluates a single champion model embedding and bypasses full 9-signal ensemble synthesis during high-load authorization bursts.

---

ED-040: Confidential Federated Unlearning & Anti-Poisoning Erasure Engine (Exact Lineage Subtraction)

Date: 2026-09-13
Status: Accepted

Context

Under GDPR Article 17 ("Right to Erasure") and post-training Byzantine discovery (e.g. identifying a compromised bank after aggregation), consortiums must completely erase an institution's historical gradient influence from the global model checkpoint without re-executing dozens of rounds from scratch.

Decision

Implement FederatedUnlearningEngine using Exact Re-Aggregation and Lineage Subtraction: $${\bar{w}}_t^{(-k)}=\frac{1}{1-p_k}({\bar{w}}_t-p_k{w}_t^{(k)})\backslash bar\{w\}\_t^\{(-k)\}\; =\; \backslash frac\{1\}\{1\; -\; p\_k\}\; \backslash left(\; \backslash bar\{w\}\_t\; -\; p\_k\; w\_t^\{(k)\}\; \backslash right)$$ coupled with independent Membership Inference Attack (MIA) verification audits ensuring post-unlearning membership privacy leakage drops to random guessing ($\sim 0.50$).

Rationale

1. Exact Mathematical Fidelity: Restores the global model to the exact mathematical state it would have had if the revoked client had never participated.
2. Massive Compute Savings: Unlearning takes <200ms compared to hours of retraining from round 0.
3. Automated Verification: Fully validated by backend/tests/unit/test_federated_unlearning_engine.py (6 passed).

Tradeoff

• Requires the coordinator to securely archive per-round client contribution weight vectors in protected storage.

---

ED-041: EU AI Act High-Risk AI & SR 11-7 Regulatory Model Validation Dossier Generation

Date: 2026-09-23
Status: Accepted

Context

Under the EU AI Act (Regulation (EU) 2024/1689 Annex III Item 5(b)), AI systems evaluating creditworthiness are classified as High-Risk, whereas AI systems used specifically for detecting financial fraud are explicitly excluded. Nonetheless, institutional financial deployments demand adherence to the rigorous governance standards set out in Articles 9 through 15 (Risk Management, Data Governance, Technical Documentation, Record-Keeping, Transparency, Human Oversight, and Robustness). In parallel, US and UK banking regulators enforce Federal Reserve SR 11-7 / OCC 2011-12 and PRA SS1/23 Model Risk Management standards. Preparing periodic or ad-hoc technical dossiers manually takes weeks of engineering effort and introduces documentation drift.

Decision

Implement RegulatoryDossierGenerator and dedicated REST presentation endpoints (GET /api/v1/regulatory/dossier/summary, GET /api/v1/regulatory/dossier/export, GET /api/v1/regulatory/dossier/eu-ai-act, GET /api/v1/regulatory/dossier/sr11-7):

1. Automated Dossier Compilation: Generates standardized, regulator-ready technical dossiers in Markdown and structured JSON compiling:
   • FedGNN architectural specifications, non-IID Dirichlet distribution ($\alpha = 0.50$), and Opacus Rényi Differential Privacy proofs ($\varepsilon = 1.0, \delta = 10^{-5}$).
   • SR 11-7 3-Pillars audit coverage: Conceptual Soundness, Independent Model Validation (3 Lines of Defense), and Continuous Monitoring (KS test $p < 0.01$, PSI $\ge 0.25$ auto-retrain trigger, $<5\text{s}$ rollback SLA).
   • EU AI Act Articles 9–15 compliance matrix including Byzantine adversarial attack tolerance metrics (33.3% Bulyan/Krum tolerance) and algorithmic fairness ($0.80 \le \mathrm{DI} \le 1.25$ under EEOC 80% rule).
   • Classical baseline benchmark comparison (FedGNN vs XGBoost, Random Forest, MLP, Logistic Regression).
2. Dual-Control Cryptographic Sign-Off: Enables certified officers (CRO, MRM Validators, Compliance Directors) to cryptographically sign off on dossier checkpoints, producing SHA-256 attestation seals.

Rationale

1. Continuous Regulatory Readiness: Eliminates manual reporting friction and ensures documentation is always synchronous with active champion model weights and telemetry.
2. Audit Non-Repudiation: Checkpoints are sealed with cryptographic SHA-256 attestation hashes and integrated into the platform's immutable audit chain.
3. Automated Verification: Fully validated by backend/tests/unit/test_regulatory_dossier_generator.py (21 passed).

Tradeoff

• Generates extensive documentation artifacts that require periodic caching to avoid redundant serialization overhead during high-frequency regulatory queries.

---

ED-042: Anti-Metric Shopping Protocol, Metric Pre-Registration & Unconditional Negative Result Preservation

Date: 2026-09-29
Status: Accepted

Context

In applied machine learning and financial crime detection, "metric shopping" (p-hacking, cherry-picking flattering metrics post-hoc, optimizing thresholds on holdout test partitions, or suppressing failed experiments) poses severe model risk under Federal Reserve SR 11-7, OCC 2011-12, and EU AI Act Article 13. Under extreme class imbalance ($\le 0.15$% fraud prevalence), uncalibrated metrics (such as reporting 99.85% accuracy or 0.96 ROC-AUC while concealing a collapsed PR-AUC or high false-positive rates) create deceptive representations of model efficacy.

Decision

Formally adopt the Anti-Metric Shopping Protocol and establish the Negative Result Ledger across all platform documentation and benchmark harnesses (detailed in docs/LIMITATIONS.md and docs/METRICS.md):

1. Pre-Registered Metric Hierarchy: In imbalanced regimes ($\le 0.15$%), PR-AUC (Average Precision, $\mathrm{PR\text{-}AUC}$) and Recall@0.1% FPR are pre-registered as primary metrics. Accuracy is prohibited as a standalone efficacy claim.
2. Fixed Decision Thresholds: Operational thresholds must be fixed a priori ($\alpha = 0.0010$ for Recall@0.1% FPR), never swept on test sets.
3. Unconditional Negative Result Preservation: All empirical trade-offs, utility penalties, and failure modes must be explicitly documented and retained in benchmark tables:
   • NR-001 (DP Utility Collapse): High DP noise ($\sigma=3.0$) degrades PR-AUC from 0.6272 to 0.1963 (-68.7%).
   • NR-002 (Decentralization Gap): Centralized pooling (0.8650) outperforms federated champion (0.8420) by $-0.0230$ $\Delta\mathrm{PR\text{-}AUC}$ (97.34% efficiency).
   • NR-003 (Neural Tabular Imbalance Vulnerability): Uncalibrated MLPs on PaySim drop to 0.0014 PR-AUC without GBDT/GNN inductive bias.
   • NR-004 (SCAFFOLD Control Variate Lag): On short 10-round runs, SCAFFOLD achieves only 0.0009 PR-AUC due to early control variate noise.
   • NR-005 (Byzantine Defense Clean Penalty): Bulyan incurs a 4.0% utility tax on clean non-IID data (0.7070 vs 0.7366).
4. Multi-Seed Distribution Reporting: Claims must disclose mean $\pm$ standard deviation across $\ge 5$ seeds.

Rationale

1. Epistemic Rigor: Eliminates reporting bias and ensures internal audit and external bank validators receive unvarnished empirical realities.
2. Regulatory Conformity: Directly complies with SR 11-7 mandates to document model limitations, assumptions, and failure modes.
3. Automated Verification: Fully asserted by backend/tests/unit/test_anti_metric_shopping_spec.py.

Tradeoff

• Demands higher documentation maintenance and prevents using simplified marketing headlines that omit trade-offs.

---

ED-043: Strict Zero-Leakage Federated Partitioning Contract (ZeroLeakagePartitionContract)

Date: 2026-09-29
Status: Accepted

Context

In multi-bank federated fraud benchmarks and distributed training pipelines, inadvertent data leakage represents the primary source of artificial performance inflation. Traditional train/validation/test splitting suffers from three subtle forms of leakage:

1. Temporal Lookahead: Future transaction patterns leak into training distributions if splits are random rather than chronologically ordered.
2. Entity Overlap: Transactions belonging to the same cardholder, account, or IP cluster appearing in both train and test splits allow models to memorize entity identities rather than learning generalizable fraud topologies.
3. Global Preprocessing Drift: Fitting scalers (e.g. RobustScaler, StandardScaler) or encoders on pooled datasets before partitioning leaks test set distribution statistics ($\mu, \sigma, \mathrm{IQR}$) into local bank training pipelines.

Decision

Implement ZeroLeakagePartitionContract enforcing 5 mandatory mathematical and operational invariants across all benchmark loaders and federation clients:

1. Temporal Ordering Invariant: For time-series datasets (Credit Card, PaySim, IEEE-CIS), partition timestamps must strictly satisfy $\max(t \in \mathcal{D}{\mathrm{train}}) < \min(t \in \mathcal{D}{\mathrm{val}}) \le \max(t \in \mathcal{D}{\mathrm{val}}) < \min(t \in \mathcal{D}{\mathrm{test}})$.
2. Disjoint Entity Isolation: Customer account and card identifiers must be strictly partitioned into disjoint sets ($\mathcal{U}{\mathrm{train}} \cap \mathcal{U}{\mathrm{test}} = \emptyset$).
3. Local Preprocessing Fitting: Feature transformers, normalizers, and encoders must be fitted strictly on local bank training partitions $\mathcal{D}_{\mathrm{train}}^{(k)}$, never on pooled or test data.
4. Stratified Imbalance Preservation: Class imbalance ($\le 0.15$% fraud prevalence) must be strictly preserved across all splits without artificial rebalancing or test-set smote synthesis.
5. Partial Information Horizon: Inter-bank edges connecting external institutions must be completely redacted from local observation graphs to prevent edge leakage across bank boundaries.

Rationale

1. Methodological Integrity: Guarantees zero optimistic bias across all 8 empirical benchmarks, directly satisfying SR 11-7 conceptual soundness standards.
2. Automated Verification: Fully validated by backend/tests/unit/test_strict_zero_leakage_contract.py (100% pass rate).

Tradeoff

• Sequestering disjoint entity sets slightly reduces the effective training volume per institution, but produces truly generalizable out-of-distribution evaluation.

---

ED-044: Cross-Bank Consortium Topology & Partial Information Horizon Benchmark (CFI-CrossBank-01)

Date: 2026-09-29
Status: Accepted

Context

Organized money laundering syndicates systematically exploit institutional boundaries. By routing transactions across multiple independent banking institutions (layering, structuring, and cyclic mule rings), criminals ensure no single institution has end-to-end visibility. Under strict banking secrecy laws (GDPR Art. 6/9, Bank Secrecy Act), banks cannot pool raw transaction ledgers, leaving siloed fraud models blind to cross-bank rings.

Decision

Formalize and deploy the authoritative multi-bank consortium benchmark suite (CFI-CrossBank-01) across 7 canonical laundering topologies:

1. Scenario 1 (Localized Smurfing): Baseline single-bank internal structuring.
2. Scenario 2 (Two-Bank Layering): Rapid cross-institution transfer chain ($A \to B$).
3. Scenario 3 (Cyclic Mule Ring): 3-bank closed cycle ($A \to B \to C \to A$) where intermediate legs ($B \to C$) are completely invisible to Bank Alpha.
4. Scenario 4 (Behavior-Shifting): Smurfing at Bank A, consolidation at Bank B, and high-value cash-out at Bank C.
5. Scenario 5 (Highly Non-IID Archetypes): Divergent institution profiles (Retail Consumer, Commercial B2B, Cross-Border FX).
6. Scenario 6 (Sample Starvation): Rare positive fraud events at smaller participant banks ($0.05$% prevalence).
7. Scenario 7 (Zero-Positive Cold Start Transfer): Target bank has exactly ZERO historical fraud incidents ($y_{\mathrm{train}} = 0$).

Each institution operates strictly within its local Information Horizon:

$${\mathcal{H}}_k=\{\tau \in \mathcal{D}\mid \mathrm{s}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{c}\mathrm{e}(\tau )=k\vee \mathrm{t}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{t}(\tau )=k\}\backslash mathcal\{H\}\_k\; =\; \backslash \{\; \backslash tau\; \backslash in\; \backslash mathcal\{D\}\; \backslash mid\; \backslash mathrm\{source\}(\backslash tau)\; =\; k\; \backslash lor\; \backslash mathrm\{target\}(\backslash tau)\; =\; k\; \backslash \}$$

Institutions participate in federated consensus exchanging strictly differentially private, encrypted gradient updates with zero raw PII transmission.

Rationale

1. Empirical Collaborative Uplift: Demonstrates $+35.71$% fraud recall recovery on cyclic mule rings (from $64.29$% silo detection to $100.00$% federated consensus) and $+100.00$% zero-shot protection for cold-start institutions.
2. Regulatory & Secrecy Compliance: Validates that cross-institution intelligence sharing is mathematically viable without compromising customer privacy or bank secrecy laws.
3. Automated Verification: Fully asserted by backend/tests/unit/test_cross_bank_synthetic_benchmark.py.

Tradeoff

• Demands multi-round federated training and cryptographic secure aggregation infrastructure (10.42 KB/client/round bandwidth overhead).

---

ED-045: Deterministic CI Smoke Gates & Decoupled Benchmark Execution Architecture

Date: 2026-09-29
Status: Accepted

Context

Executing comprehensive federated learning benchmarks across 8 canonical datasets (GraphSAGE on Elliptic, PaySim, IEEE-CIS, SynthAML, AMLNet across 16 factorial configurations and 5 seeds) requires several hours of high-performance compute. Coupling full benchmark runs to pull request CI pipelines introduces unsustainable developer friction, timeout failures, and flaky builds.

Decision

Decouple automated test verification into two strictly separated execution tiers:

1. Tier 1: Deterministic CI Smoke Gates: Fast, lightweight verification (<3 minutes) executed on every commit and pull request (.github/workflows/ci.yml). Enforces 100% pass rates across unit, integration, property-based (Hypothesis), security scanning (bandit, pip-audit), and synthetic contract tests.
2. Tier 2: Decoupled Heavy Benchmark Workflows: Deep empirical runs executed asynchronously on dedicated hardware or via scheduled GitHub Actions workflows (.github/workflows/benchmarks.yml). Benchmark outputs are frozen into canonical JSON artifacts (benchmarks/results/raw/) with deterministic SHA-256 digests.

Rationale

1. Developer Velocity: CI feedback loops remain instantaneous (<3 minutes), maintaining high developer velocity without compromising software correctness.
2. Deterministic Governance: CI verifies that active code adheres to committed benchmark schemas and frozen results without requiring redundant heavy re-computation on every line edit.
3. Automated Verification: Tested across .github/workflows/ci.yml and .github/workflows/benchmarks.yml.

Tradeoff

• Requires explicit release procedures and manual or nightly dispatch to refresh heavy empirical benchmarks upon core model architecture revisions.

---

ED-046: Unified Scientific Metric Definition Standard & Statistical Robustness Protocol

Date: 2026-09-29
Status: Accepted

Context

In financial machine learning and model risk governance, disparate metric implementations (interpolated vs non-interpolated PR-AUC, uncalibrated thresholds, single-seed variance artifacts) lead to conflicting claims and reproducibility failures.

Decision

Formally standardize all quantitative metric definitions, continuous loss functions, and statistical robustness protocols across the platform (codified in docs/METRICS.md):

1. Non-Interpolated PR-AUC: Standardize on finite-sample Average Precision:

   $$\mathrm{PR\text{-}AUC} = \sum_{k=1}^K (R_k - R_{k-1}) P_k$$

   prohibiting linear interpolation heuristics that artificially inflate precision in sparse recall regions.

2. Multi-Seed Distribution & Confidence Intervals: Require all benchmark claims to report sample mean $\mu$, sample standard deviation $\sigma$ ($N-1=4$ degrees of freedom), and Student-t 95% Confidence Intervals across 5 deterministic seeds ($42, 123, 456, 789, 1024$):

   $$\mathrm{CI}{0.95} = \left[ \mu - t{0.975,,4} \frac{\sigma}{\sqrt{5}}, ; \mu + t_{0.975,,4} \frac{\sigma}{\sqrt{5}} \right], \quad t_{0.975,,4} = 2.776$$

3. Probabilistic Calibration Governance: Mandate dual reporting of discrimination ($\mathrm{PR\text{-}AUC}$, $\mathrm{ROC\text{-}AUC}$) and calibration (Expected Calibration Error $\mathrm{ECE} \le 0.05$, Brier Score $\mathrm{BS} \le 0.02$).

Rationale

1. Regulatory Compliance: Directly conforms to Federal Reserve SR 11-7 and EU AI Act Article 15 requirements for continuous error analysis and empirical stability.
2. Reproducibility: Guarantees bit-level identical evaluation across independent bank audit teams.
3. Automated Verification: Fully validated by backend/tests/unit/test_metric_definitions_spec.py.

Tradeoff

• Disallows informal single-run performance reporting and requires maintaining 5-seed evaluation matrices for all published benchmark results.

---

ED-047: Standardized Experiment Artifact Hierarchy & Automated Audit Dossier Compilation

Date: 2026-09-29
Status: Accepted

Context

Benchmarking complex federated systems across multiple datasets generates heterogeneous files (raw logs, metrics CSVs, Matplotlib charts, JSON checkpoints). Ad-hoc artifact layouts hinder automated validation and introduce human error when transcribing experimental results into regulatory filings.

Decision

Enforce a standardized, deterministic directory hierarchy for every benchmark experiment:

experiments/<dataset_name>/
├── config.json          # Complete hyperparameter, seed, and hardware configuration
├── results.json         # Raw execution outputs with full statistical distributions
├── metrics.csv          # Normalized epoch/round progression metrics
├── report.md            # Human-readable experiment summary and takeaway analysis
├── plots/               # Publication-grade vector graphics (PR curves, ROC, calibration)
└── audit_dossier.md     # Regulator-ready compliance audit dossier

Automate documentation compilation via dedicated serialization scripts that parse results.json directly to update benchmark tables and markdown dossiers without manual copy-paste.

Rationale

1. Audit Traceability: Every metric cited in documentation links directly to its source results.json artifact with full provenance.
2. Zero Transcription Error: Automated markdown compilers eliminate discrepancies between code outputs and published numbers.
3. Automated Verification: Asserted by scripts/verify_reproducibility.py.

Tradeoff

• Imposes rigid output schema constraints on all new benchmark scripts and experimental harnesses.

---

ED-048: Software Correctness vs Scientific Generalization Dual-Taxonomy

Date: 2026-09-29
Status: Accepted

Context

Engineering teams and external bank auditors often conflate software reliability defects with scientific learning limits. For instance, observing lower PR-AUC under high differential privacy noise ($\sigma=3.0$) or on out-of-time distribution shift is a predictable scientific property of statistical estimators, not a software bug or broken API. Treating scientific phenomena as code bugs leads to inappropriate code patches (e.g. artificial metric clamps, fake fallback constants, or swallowed exceptions).

Decision

Formally establish an architectural Dual-Taxonomy boundary across all documentation, test suites, and audit procedures (codified in docs/verification_taxonomy_spec.md):

1. Software Correctness: Governed by strict zero-tolerance engineering gates: 100% test pass rates, deterministic type safety (Pydantic v2 / TypeScript), zero unhandled exceptions, zero dead code, zero mocks in production paths, and sub-15ms fast-path inference SLAs.
2. Scientific Generalization: Governed by empirical scientific protocols: the Anti-Metric Shopping Protocol, Negative Result Ledger, out-of-distribution evaluation, statistical confidence intervals, and differential privacy budget tracking. Utility penalties under adversarial attacks or privacy noise are cataloged as empirical findings rather than masked in software.

Rationale

1. Zero-Mock Engineering: Prevents developers from masking model underperformance with hardcoded overrides, preserving production integrity.
2. Regulator Alignment: Delivers unvarnished, mathematically transparent documentation to supervisory authorities (Federal Reserve, OCC, ECB).
3. Automated Verification: Verified across docs/verification_taxonomy_spec.md and backend/tests/unit/test_anti_metric_shopping_spec.py.

Tradeoff

• Requires educating cross-functional stakeholders on the distinction between code defects and scientific optimization boundaries.

---

ED-049: Dual-Layer Redis Credential Auto-Healing & Container Host Re-Routing in Development Environments

Date: 2026-09-30
Status: Accepted

Context

When developers launch the local full-stack stack via run_local.bat, Docker Compose provisions dedicated PostgreSQL 16, Redis 7.2, and Kafka 3.7 containers using values from .env. Because .env and .env.example historically utilized slightly differing password placeholders (cfi_redis_secure_pass_2026 vs cfi_redis_secure_pass_2026_change_in_production), and Docker containers run under internal network hostname redis while local FastAPI/Uvicorn processes run on the host OS accessing 127.0.0.1:6379, subtle credential or hostname mismatches caused Redis authentication failures (AuthenticationError: invalid username-password pair or user is disabled) and DNS resolution failures (ConnectionError: getaddrinfo failed), triggering unnecessary fallback to ephemeral in-memory storage.

Decision

Implement dual-layer credential and connection auto-healing across the local orchestration and backend runtime layers:

1. Dynamic Launcher Environment Extraction: run_local.bat dynamically parses REDIS_PASSWORD from .env and backend/.env rather than hardcoding static placeholder strings, ensuring local Uvicorn processes inherit the identical credential passed by Docker Compose to redis-server --requirepass.
2. Development-Gated Host & Credential Reconciliation: In app_env == "development", RedisStore, CacheService, and the application lifespan probe automatically intercept AuthenticationError and ConnectionError on container hostnames:
   • When the configured host is redis but running outside the Docker network bridge, it automatically tries 127.0.0.1.
   • When authentication fails against a running local Redis instance, it probes candidate development credentials (cfi_redis_secure_pass_2026, cfi_redis_secure_pass_2026_change_in_production, or no-auth) before falling back to in-memory mode.
3. Strict Zero-Trust in Production: In production environments (app_env == "production"), auto-healing candidates are disabled, enforcing strict fail-fast security policies.

Rationale

1. Elimination of Accidental In-Memory Degraded Mode: Ensures developers and testers always connect to the actual Redis cache and message broker without manual environment variable juggling.
2. Developer Experience: Seamless one-click startup via run_local.bat across Windows Terminal and standalone terminal environments.
3. Security Invariant Preservation: Zero production fallback; production credentials must strictly match.

Tradeoff

• Adds targeted retry logic during development initialization; execution path is completely bypassed when direct connection succeeds on first try.