FedProx Algorithm Specification
1. Problem Solved
FedProx (Li et al., 2020) addresses two major bottlenecks in federated banking networks:
- Statistical Heterogeneity (Non-IID Skew): Varying client fraud rates (e.g., retail bank vs commercial credit card issuer).
- System Heterogeneity (Stragglers): Variable computing resources and network speeds across participating institutions.
FedProx adds a proximal regularization term to the local objective function to constrain local updates from drifting too far from the global consensus model.
2. Implementation in CF-Intelligence
Mathematical Formulation: Bank $k$ optimizes the regularized surrogate objective at round $t$:
$$\min_{w \in \mathbb{R}^d} h_k(w; w_t) \triangleq F_k(w) + \frac{\mu}{2} |w - w_t|_2^2$$
where $\mu \ge 0$ is the proximal regularization hyperparameter (default $\mu = 0.01$).
Gradient Update: During local SGD on mini-batch $\mathcal{B}$, the effective gradient is:
$$g_k(w) = \nabla F_k(w; \mathcal{B}) + \mu (w - w_t)$$
When a bank experiences system slowdown or timeout, it can return an inexact solution ($\gamma$-inexact) after completing partial local epochs without destabilizing the global model.
3. Threat Model & Security Assumptions
- Client Drift Bounds: Guarantees bounded deviation $|w_k - w_t|_2 \le \frac{G}{\mu}$ even when local distributions are disjoint.
- Privacy Stance: Does not inherently encrypt or noise gradients; must be paired with Opacus Differential Privacy and Curve25519 SecAgg.
4. Operational Limitations
- Hyperparameter Sensitivity: Setting $\mu$ too large inhibits local learning, reducing convergence speed. Setting $\mu$ too small degenerates FedProx into standard FedAvg.
- Gradient Computation: Incurs marginal additional memory and compute overhead to store and compute Euclidean distance against the frozen global weight snapshot $w_t$.
5. Non-IID Dirichlet Sweep Empirical Evaluation
The platform evaluates FedProx ($\mu = 0.01$) against FedAvg and SCAFFOLD under varying degrees of Dirichlet label and feature skew $\alpha \in {0.1, 0.5, 1.0}$:
- Experiment Runner:
experiments/ablations/dirichlet_sweep.py - Empirical Dossier:
experiments/ablations/audit_dossier.md - Publication Figure:
docs/figures/benchmark_fl_convergence.png
Empirical Results Summary across 10 Federation Rounds
| Skew Regime | Dirichlet $\alpha$ | FedProx PR-AUC | FedProx ROC-AUC | Validation Loss | Communication (MB) | Drift Suppression |
|---|---|---|---|---|---|---|
| Pathological Extreme Skew | $\alpha = 0.1$ | 0.2776 | 0.8628 | 0.0969 | 0.147 MB | Bounded ($|w_k - w_t|_2 \le 0.18$) |
| Moderate Consortium Skew | $\alpha = 0.5$ | 0.2285 | 0.9185 | 0.0969 | 0.147 MB | Stable convergence |
| Mild Statistical Skew | $\alpha = 1.0$ | 0.2180 | 0.8959 | 0.1068 | 0.147 MB | Baseline alignment |
Key Takeaway: Under extreme statistical heterogeneity ($\alpha = 0.1$), FedProx restrains client gradient drift, preventing the loss oscillation characteristic of standard FedAvg without requiring the stateful control variate memory of SCAFFOLD.
6. Test Suite Verification
- Unit Tests:
backend/tests/unit/test_fl_engine.py - Dirichlet Sensitivity Tests:
backend/tests/unit/test_dirichlet_sweep.py - Benchmark Runner:
benchmarks/runners/run_fl_benchmark.py