| # Formal Limitations, Anti-Metric Shopping Protocol & Negative Result Ledger |
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| **Document Reference:** `CFI-SPEC-LIMITATIONS-2026-V1` |
| **Applicable Governance Frameworks:** Federal Reserve SR 11-7 / OCC 2011-12 (Model Risk Management - Limitations, Assumptions and Validation), EU Artificial Intelligence Act (Regulation (EU) 2024/1689) Article 13 (Transparency and Information to Deployers), ACM/IEEE Guidelines on Transparent Machine Learning Reporting, NeurIPS Paper Checklist Standards. |
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| ## 1. Epistemological Rationale & Anti-Metric Shopping Protocol |
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| ### 1.1 The Problem of Metric Shopping in Machine Learning |
| In applied machine learning, financial technology, and academic benchmarking, **metric shopping** (selective reporting, p-hacking, or post-hoc metric cherry-picking) presents a critical model risk: |
| 1. **Selective Highlighting**: Teams evaluate multiple evaluation metrics (Accuracy, ROC-AUC, PR-AUC, $F_1$, Recall@FPR) and publish only the flattering numbers. Under extreme class imbalance ($\le 0.15$% fraud prevalence), reporting an uncalibrated accuracy of 99.85% or an inflated ROC-AUC of 0.96 conceals catastrophic real-world failure, where thousands of false alarms drown human investigator queues and zero complex money laundering is detected. |
| 2. **Post-Hoc Threshold Optimization**: Selecting classification decision thresholds ($\theta$) by sweeping the test set to maximize an $F_1$-score artificially inflates claimed effectiveness while guaranteeing out-of-sample operational collapse. |
| 3. **Suppression of Negative Findings**: Omitting experiments where privacy noise destroyed model convergence, or concealing algorithms that lagged behind naive baselines, creates dangerous confirmation bias and deceives institutional risk committees. |
| 4. **Selective Seed Reporting**: Executing dozens of random initializations and reporting only the best-performing run without disclosing the variance distribution. |
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| ### 1.2 The Anti-Metric Shopping Protocol |
| To enforce scientific transparency, CF-Intelligence mandates the following binding protocol across all documentation, benchmark runners, and pull requests: |
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| ``` |
| βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| β ANTI-METRIC SHOPPING PROTOCOL RULES β |
| βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ |
| β 1. Mandatory Metric Hierarchy Pre-Registration β |
| β - Extreme Imbalance (<=0.15%): Primary = PR-AUC & Recall@0.1%FPR. β |
| β - ROC-AUC and Accuracy designated strictly as secondary diagnostic aids. β |
| βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ |
| β 2. Pre-Fixed Decision Thresholds β |
| β - Operational thresholds MUST be fixed a priori (e.g. alpha = 0.0010 for β |
| β Recall@0.1%FPR, theta = 0.50 default), NEVER tuned on test sets. β |
| βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ |
| β 3. Unconditional Negative Result Preservation β |
| β - Utility collapse, baseline lag, or noise penalties MUST be published. β |
| β - Null results MUST use strict "-" representation, never omitted rows. β |
| βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ |
| β 4. Multi-Seed Robustness & Variance Disclosure β |
| β - Statistical claims MUST report mean +/- std over >= 5 distinct seeds. β |
| βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ |
| ``` |
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| 1. **Pre-Registered Metric Hierarchy**: |
| - For fraud and AML detection (imbalance $\le 0.15$%), **Precision-Recall Area Under Curve (PR-AUC / Average Precision)** and **Recall at Fixed False Positive Rate (Recall @ 0.1% FPR)** are pre-registered as primary evaluation metrics. |
| - ROC-AUC and Brier Score ($\mathrm{BS}$) are classified as secondary diagnostic indicators. |
| - Overall Accuracy is strictly prohibited as a standalone efficacy claim. |
| 2. **Pre-Fixed Operational Thresholds**: |
| - Decision thresholds must be calibrated strictly on training/validation partitions or set to institutional operational SLAs ($\alpha_{\mathrm{target}} = 0.01$% or $0.1$%). Sweeping thresholds on holdout test partitions to maximize post-hoc $F_1$ is strictly forbidden. |
| 3. **Unconditional Negative Result Preservation**: |
| - Any experimental configuration where federated learning, differential privacy, or Byzantine defenses lag behind baselines must be retained in public tables and reports with explicit negative deltas ($\Delta < 0$). |
| 4. **Multi-Seed Distribution Reporting**: |
| - Benchmarks must disclose mean and sample standard deviation across standardized random seeds (e.g. `[42, 123, 456, 789, 2025]` for 5-seed benchmarks such as CrossBank v2, or `[42, 123, 456]` for 3-seed real-data benchmarks), detailing both champion and worst-case convergence behavior. |
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| --- |
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| ## 2. Unconditional Negative Result & Trade-Off Ledger |
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| In compliance with the Anti-Metric Shopping Protocol, this section explicitly documents the empirical trade-offs, utility penalties, and architectural bottlenecks identified across our experimental benchmark suite. |
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| ### 2.1 Negative Result NR-001: Differential Privacy Utility Collapse under Strong Noise ($\sigma \ge 3.0$) |
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| #### Empirical Observation |
| In [`benchmarks/results/raw/dp_privacy_utility_tradeoff.json`](../benchmarks/results/raw/dp_privacy_utility_tradeoff.json), evaluating Gaussian DP-SGD noise multipliers $\sigma \in [0.0, 3.0]$ reveals a steep Pareto trade-off between privacy loss ($\epsilon$) and fraud detection power ($\mathrm{PR\text{-}AUC}$): |
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| | Noise Multiplier ($\sigma$) | RDP Privacy Loss ($\epsilon, \delta=10^{-5}$) | Holdout PR-AUC | Utility Delta ($\Delta \text{PR-AUC}$) | Status & Trade-Off Analysis | |
| | :---: | :---: | :---: | :---: | :--- | |
| | $\sigma = 0.0$ | $\infty$ (Non-Private Ceiling) | **0.6272** | Baseline | Full gradient utility; zero privacy defense | |
| | $\sigma = 0.5$ | $\epsilon = 8.4210$ | **0.5891** | $-0.0381$ (-6.1%) | Mild utility loss; weak privacy boundary | |
| | $\sigma = 1.0$ | $\epsilon = 3.1450$ | **0.5124** | $-0.1148$ (-18.3%) | Standard academic benchmark point | |
| | $\sigma = 1.5$ | $\epsilon = 1.8920$ | **0.4208** | $-0.2064$ (-32.9%) | Production operational boundary | |
| | $\sigma = 2.0$ | $\epsilon = 1.2410$ | **0.3150** | $-0.3122$ (-49.8%) | High privacy; severe detection degradation | |
| | $\sigma = 3.0$ | $\epsilon = 0.6272$ | **0.1963** | **$-0.4309$ (-68.7%)** | **Severe Utility Collapse**: -68.7% loss | |
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| > [!NOTE] |
| > **Archival Note on Legacy Prototype Data & Canonical Frontier**: |
| > The table above records the historical prototype run (10-feature algebraic centroid prototype without per-sample clipping). The canonical multi-seed Opacus DP-SGD neural benchmark ($N=20{,}000$, per-sample gradient clipping $C=1.0$, PRV accountant) achieves $\mathrm{PR\text{-}AUC} = 0.8965 \pm 0.0078$ at $\sigma=0.0$ and degrades to $0.3465 \pm 0.1580$ at $\sigma=3.0$ ($\epsilon=0.3497$, $-61.35\%$ relative loss) - confirming the identical fundamental negative result (utility collapse under strong privacy noise) under production-grade PyTorch Opacus execution. Full canonical results: [`dp_privacy_utility_tradeoff.json`](../benchmarks/results/raw/dp_privacy_utility_tradeoff.json). |
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| #### Mathematical & Operational Reality |
| - **Why It Happens:** Differential privacy injects spherical Gaussian noise $\mathcal{N}(0, \sigma^2 C^2 \mathbf{I})$ into clipped gradient updates. In extreme class imbalance, the gradient signal corresponding to rare fraudulent samples is minuscule relative to majority legitimate traffic. At $\sigma \ge 3.0$, the perturbation variance swamps the minority gradient coordinates, destroying decision boundary refinement. |
| - **Operational Reality:** Banks cannot operate at extreme differential privacy ($\epsilon < 1.0$) in real-time fraud scoring without surrendering more than two-thirds of their fraud detection capability. The platform defines $\epsilon \le 1.0, \delta = 10^{-5}$ as a research target / intended configuration, while empirical evaluations benchmark points from $\epsilon = 1.858$ down to strong utility-collapse regimes. Real-world production deployment remains subject to future institutional risk appetite. |
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| --- |
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| ### 2.2 Negative Result NR-002: Decentralization & Centralization Performance Gap ($\Delta_{\mathrm{privacy}}$) |
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| #### Empirical Observation |
| In [`enterprise_benchmark_report.md`](enterprise_benchmark_report.md) Section 3.2, comparing a theoretically pooled centralized database against the decentralized federated champion shows a persistent performance deficit: |
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| | Evaluation Paradigm | Privacy Perimeter | PR-AUC | ROC-AUC | Recall @ 0.1% FPR | Centralization Gap ($\Delta_{\mathrm{privacy}}$) | |
| | :--- | :--- | :---: | :---: | :---: | :---: | |
| | **Centralized Upper Bound (GBDT)** | Illegal Raw Data Pooling | **0.8650** | 0.9840 | 66.50% | Baseline Upper Bound | |
| | **Federated Champion (FedAvg/FedProx)** | Zero Raw PII (Decentralized) | **0.8420** | 0.9750 | 62.40% | **$-0.0230$ (-2.7% Gap)** | |
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| #### Mathematical & Operational Reality |
| - **Why It Happens:** Centralized gradient boosting has immediate, unconstrained access to joint feature co-occurrence matrices across all institutions. Federated learning must optimize across disparate local non-IID SGD steps with bounded local epochs and DP clipping, incurring an inherent decentralized optimization penalty ($\Delta_{\mathrm{privacy}} = -0.0230$). |
| - **Honest Perspective:** While federated intelligence captures 97.34% of the theoretical ceiling, decentralization does not match 100% of centralized pooling. Claims that federated learning completely matches centralized pooling without any penalty are scientifically unfounded. |
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| --- |
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| ### 2.3 Negative Result NR-003: Deep Neural MLP Vulnerability to Extreme Imbalance on Tabular Data |
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| #### Empirical Observation |
| In [`enterprise_benchmark_report.md`](enterprise_benchmark_report.md) Section 3.4 (PaySim benchmark across $30{,}000$ transactions with 0.05% fraud prevalence), deep multi-layer perceptrons without tree-based ensembling or specialized graph topology failed completely: |
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| | Model Architecture | Optimization Paradigm | Holdout PR-AUC | Holdout ROC-AUC | Recall @ 0.1% FPR | Verdict | |
| | :--- | :--- | :---: | :---: | :---: | :--- | |
| | **Centralized Deep MLP** | Pooled PyTorch Neural Net | **0.0014** | 0.6219 | **0.00%** | β **Failure on Tabular Imbalance** | |
| | **Centralized GBDT** | Pooled LightGBM / XGBoost | **0.6668** | 0.6677 | **66.67%** | β
**Robust Tabular Boundary** | |
| | **Federated Champion (FedAvg)** | Decentralized GraphSAGE+MLP | **0.1184** | 0.8700 | **33.33%** | β οΈ **Requires Graph Inductive Bias** | |
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| #### Mathematical & Operational Reality |
| - **Why It Happens:** Standard continuous backpropagation with cross-entropy loss on unweighted raw tabular streams collapses predicted fraud probabilities toward the empirical prior ($\sim 0.0005$). Without inductive relational graph biases (GraphSAGE) or decision tree partitioning (GBDT), neural networks alone are ill-suited for raw tabular fraud detection. |
| - **Remediation Implemented:** The platform pairs neural embeddings with GraphSAGE inductive topology and hybrid gradient boosting, rather than relying on pure tabular MLPs. |
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| --- |
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| ### 2.4 Negative Result NR-004: SCAFFOLD Control Variate Drift on Short Communication Horizons |
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| #### Empirical Observation |
| In PaySim federated optimization across 10 communication rounds with Dirichlet skew ($\alpha = 0.50$): |
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| | Federated Optimizer | Strategy Classification | 10-Round Holdout PR-AUC | Communication / Round | Convergence Stability | |
| | :--- | :--- | :---: | :---: | :--- | |
| | **FedAvg** | Parameter Averaging | **0.1184** | $0.024\text{ MB}$ | Stable gradient progress | |
| | **FedProx ($\mu=0.01$)** | Proximal Regularization | **0.0348** | $0.024\text{ MB}$ | Bounded drift | |
| | **SCAFFOLD** | Client/Server Control Variates | **0.0009** | $0.048\text{ MB}$ ($2\times$ payload) | β **Early Horizon Destabilization** | |
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| #### Mathematical & Operational Reality |
| - **Why It Happens:** SCAFFOLD relies on estimating client drift control variates ($c_i$) and global drift ($\bar{c}$). On short training runs ($T \le 10$ rounds), inaccurate early control variates introduce noisy corrective vectors into local SGD steps, destabilizing learning. Furthermore, SCAFFOLD doubles bandwidth overhead per round ($2\times$ payload). |
| - **Honest Perspective:** SCAFFOLD is not a universal panacea for Non-IID skew; in short-duration federated rounds, simple FedAvg or FedProx with proximal regularization consistently outperforms SCAFFOLD. |
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| --- |
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| ### 2.5 Negative Result NR-005: Byzantine Defense Utility Penalty on Benign Non-IID Skew |
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| #### Empirical Observation & Canonical Multi-Round Benchmark |
| Evaluated across two complementary benchmark suites: |
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| 1. **Canonical Multi-Round 72-Condition Benchmark** ([`benchmarks/results/raw/byzantine_federated_canonical.json`](../benchmarks/results/raw/byzantine_federated_canonical.json), Status: `CANONICAL`): |
| - Evaluated on real Credit Card Fraud tabular data partitioned into 12 simulated bank clients under Non-IID Dirichlet distribution ($\alpha = 0.50$, $\min(\text{samples}) = 50$, zero-positive clients permitted, 10 federated rounds, $N=3$ seeds). |
| - Under clean baseline conditions ($f=0$), honest FedAvg achieves PR-AUC $0.7179 \pm 0.0207$. Robust aggregators incur a small clean utility penalty or maintain parity: Trimmed Mean ($\beta=0.20$) achieves $0.7177 \pm 0.0183$ ($\Delta = -0.0002$), Coordinate Median achieves $0.7145 \pm 0.0150$ ($\Delta = -0.0034$), Bulyan achieves $0.7161 \pm 0.0205$ ($\Delta = -0.0018$), Multi-Krum achieves $0.7118 \pm 0.0253$ ($\Delta = -0.0061$), and Single Krum achieves $0.6729 \pm 0.0270$ ($\Delta = -0.0450$). |
| - Under evaluated scaled sign-inversion attack ($\times -3.0$, $f=2$ Byzantine nodes): FedAvg collapses to $0.5279 \pm 0.3140$ (with Seed 42 catastrophic collapse to $0.1653$), while Trimmed Mean retains $0.7141 \pm 0.0129$ ($99.54\% \pm 4.05\%$ mean retention relative to clean FedAvg) and Multi-Krum retains $0.7061 \pm 0.0341$ ($98.48\% \pm 6.93\%$). |
| - Disclosed seed collapses: Single Krum collapsed to $0.0011$ on Seed 456; ALIE-style perturbations collapsed Coordinate Median ($0.2045$) and Bulyan ($0.2220$) on Seed 123. |
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| 2. **Historical Single-Seed Prototype (Quarantined)** ([`benchmarks/results/raw/byzantine_benchmark_sign_inversion.json`](../benchmarks/results/raw/byzantine_benchmark_sign_inversion.json), Status: `HISTORICAL_QUARANTINED`): |
| - Evaluated an early 10-client prototype on synthetic Gaussian data ($0.7344 / 0.7369 \approx 99.66\% \approx 99.7\%$). Retained strictly for archival traceability: |
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| | Aggregation Method | Clean Non-IID PR-AUC | Adversarial Attack Resilience | Clean Data Efficiency | Clean Penalty ($\Delta$) | Status | |
| | :--- | :---: | :---: | :---: | :---: | :---| |
| | **Naive FedAvg** | **0.7366** | 0.0000 (Collapses under Sign-Flip) | 100.0% | Baseline | Historical Prototype | |
| | **Coordinate Trimmed Mean** | **0.7344** | 0.7344 (Tolerates 20% poisoned) | 99.7% | $-0.0022$ (-0.3%) | Quarantined Archive | |
| | **Multi-Vector Krum** | **0.7257** | 0.7257 (Tolerates Byzantine nodes) | 98.5% | $-0.0109$ (-1.5%) | Quarantined Archive | |
| | **Bulyan Aggregator** | **0.7070** | 0.7070 (Provable resilience) | 95.9% | **$-0.0296$ (-4.0%)** | Quarantined Archive | |
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| #### Mathematical & Operational Reality |
| - **Why It Happens:** Byzantine-robust aggregators (Krum, Bulyan, Trimmed Mean) filter out candidate vectors located in the geometric periphery of the client update manifold. Under Non-IID Dirichlet skew, a bank with a unique legitimate transaction specialty (e.g. high-volume cross-border wires) produces legitimate gradients that reside in the geometric tail. Aggregators periodically discard these honest updates as suspected poisoning attempts. |
| - **Operational Reality:** Robustness against malicious poisoning acts as an insurance policy: it guarantees survival under attack, but incurs a clean utility tax on benign data, and cannot guarantee universal immunity across all adversarial distribution shifts or seed realizations. |
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| --- |
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| ## 3. Systemic & Architectural Limitations (What This Is NOT) |
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| To satisfy the transparency requirements of **Federal Reserve SR 11-7** and **EU AI Act Article 13**, the boundaries of this repository are formally stated: |
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| ### 3.1 Non-Production Demonstration & Benchmark Basis |
| - **Not Deployed in Live Financial Rails:** CF-Intelligence is an advanced research platform, reference implementation, and engineering demonstration. It has **never been deployed in live bank production**, connected to real SWIFT FIN rails, or used to block actual consumer payment transactions. |
| - **Synthetic & Public Benchmark Basis:** All empirical results are derived from synthetic multi-bank data generators and canonical public datasets (PaySim, IEEE-CIS, Elliptic Bitcoin, IBM AMLSim, SynthAML, AMLNet). While these datasets represent industry-standard benchmarks, they cannot capture all proprietary operational nuances of live core banking systems. |
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| ### 3.2 Single-Maintainer Portfolio & Engineering Proof-of-Concept |
| - **Single Author / Maintainer:** This repository is conceived, engineered, and maintained entirely by a single software engineer (**Yusuf ΓalΔ±ΕΔ±r**). |
| - **Not Backed by a Commercial Consortium:** Mentions of "Bank A", "Bank B", "Bank C", "JPMorgan Node", "HSBC Node", or "Consortium Quorum" represent simulated architectural tenants in software, not active corporate partnerships or commercial joint ventures. |
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| ### 3.3 Regulatory Inspiration vs. Formal Certification |
| - **Conceptual Exploration, Not Legal Certification:** Architecture discussions regarding GDPR Article 17, EU AI Act High-Risk requirements, FinCEN SAR e-filing, or EPC SEPA recall rulebooks represent technical explorations of compliance patterns. The software has **not undergone formal third-party compliance audits** or certification by regulatory authorities (FinCEN, BaFin, FCA, MAS). |
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| ### 3.4 Hardware-Emulated Security vs. Bare-Metal Silicon |
| - **Software Emulation Layer:** Production deployments require physical Hardware Security Modules (PKCS#11 HSMs), bare-metal Intel SGX / AWS Nitro Enclaves, and live distributed Apache Kafka clusters. In local development and automated CI/CD testing, the platform utilizes authentic software emulators (`SoftwareHSMSignerEngine`, `SoftwareEmulatedTEEDriver`, in-memory message buses). |
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| ### 3.5 Explainability & Manifold Constraints |
| - **Bounded SHAP Evaluation Budget:** To satisfy sub-50ms API SLAs, SHAP KernelExplainer evaluates a bounded sample budget ($N=100$) over background reference sets ($N=30$), rather than calculating exhaustive exponential Shapley permutations ($2^D$). |
| - **Discrete Counterfactual Search:** Counterfactual explanations search a greedy discrete perturbation space over domain-mutable features, rather than performing continuous gradient descent along an empirical data manifold. |
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| ### 3.6 Single-Host Consortium Development Sandbox (`LOCAL_HTTP_SANDBOX`) |
| - **Development Sandbox Demarcation:** The integrated 10-service Docker deployment (`docker-compose.consortium.yml`) operates as a single-host development sandbox on a local bridge network (`cfi-consortium-net`). While it verifies multi-container coordination, OpenMined PSI 2.0.6 workflows, and sovereign `0600` file-mounted key custody, it does NOT provide physical multi-cloud network isolation or hardware security (HSM) enclaves. It is classified as `LOCAL_HTTP_SANDBOX`. |
| - **Redis Resilience & Ephemeral Degradation:** If the Redis container experiences network severance or downtime, the caching layer degrades gracefully to bounded in-memory caching while advertising `durability="ephemeral"` via the `/health` endpoint. Dynamic background auto-reconnection (`_attempt_auto_reconnect`, 1.0s cooldown) restores `durability="durable"` once Redis connectivity resumes, eliminating the need for process restarts. |
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| ## 4. Epistemic Vulnerability Matrix |
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| | Vulnerability Domain | Technical Failure Mode | Root Cause & Mechanism | Platform Safeguard & Mitigation | Residual Operational Risk | |
| | :--- | :--- | :--- | :--- | :--- | |
| | **Statistical Non-IID Skew** | Client gradient divergence / weight oscillation | Extreme Dirichlet parameter ($\alpha \le 0.05$) creates disjoint local label support | Proximal regularization (`FedProx`), learning rate decay, adaptive round quotas | Convergence delay; lower final PR-AUC on minority banks | |
| | **Differential Privacy** | Complete utility destruction | Noise multiplier $\sigma > 2.0$ injected into low-magnitude minority coordinates | Adaptive DP auto-scaling, RDP moments accounting circuit breaker | Cannot achieve sub-unit $\epsilon$ without severe utility loss | |
| | **Adversarial Poisoning** | Byzantine coordinator hijacking | Coordinated Sybil nodes submit sign-flipped gradients | Coordinate Trimmed Mean, Krum, Bulyan distance selection | Clean-data utility penalty (1.5% - 4.0% loss); requires $n \ge 4f+3$ | |
| | **Concept Drift** | Stale model decision boundaries | Fraudsters alter structuring velocity and account hops | Automated retraining triggers ($\mathrm{PSI} > 0.20$, KS test $p < 0.05$) | Retraining lag between alert trigger and global round aggregation | |
| | **Entity Resolution** | False positive cross-bank graph links | MinHash LSH collision on sparse identity tokens | Type-salted HMAC-SHA256 hashing, secondary Jaro-Winkler disambiguation | Under-clustering on highly obfuscated mule account networks | |
| | **Concurrency & Gateway** | ASGI event loop thread contention under load | Python GIL saturation during high-concurrency requests ($C \ge 100$) | Multi-worker Gunicorn deployment, Redis JIT caching, circuit breakers | Micro-latency spikes at peak burst traffic ($p_{99} > 80\text{ ms}$) | |
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| --- |
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| ## 5. Master Reproducibility & Integrity Verification Attestation |
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| To establish complete transparency regarding what is empirically verified versus simulated, the repository provides an automated master reproducibility sweep: |
| - **Verification Harness:** [`scripts/verify_reproducibility.py`](../scripts/verify_reproducibility.py) |
| - **One-Command CLI:** `make reproduce-verify` or `python scripts/verify_reproducibility.py --all` |
| - **Automated Test Suite:** [`backend/tests/unit/test_reproducibility_verifier.py`](../backend/tests/unit/test_reproducibility_verifier.py) (10 tests, 100% passing) |
| - **Audit Coverage:** 38 / 38 items verified across all 6 canonical categories (100.0% pass rate) |
| - **Formal Attestation:** Certified reproducible in Section 8 of [`docs/engineering-audit.md`](engineering-audit.md) |
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