Regulatory-Driven Federated Learning: A Multi-Objective Approach to Compliance and Ethical AI in Financial Systems

Abstract

Federated learning (FL) allows multiple institutions to train a shared model without exchanging raw data, which makes it attractive for privacy-sensitive domains such as finance. Deploying FL across jurisdictions, however, remains difficult: privacy regimes such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) impose different, and sometimes conflicting, obligations on data handling. This paper proposes a regulatory-driven FL framework that treats compliance and fairness as first-class optimization objectives rather than as afterthoughts. The framework combines locally trained models with record-level differential privacy, secure aggregation of masked model updates, and a multi-objective loss that balances predictive accuracy, jurisdiction-specific compliance constraints, and group-fairness criteria, followed by a post-hoc bias audit that corrects residual disparities. We implement the complete pipeline and evaluate it on a synthetic cross-jurisdictional credit-scoring benchmark with three regions governed by GDPR-, CCPA-, and locally-styled privacy budgets, comparing against centralized learning and baseline federated averaging over five random seeds. The proposed framework keeps every region within its differential-privacy budget (ε = 1.58, 2.41, and 3.32 against caps of 2.0, 3.0, and 4.0 at δ = 10⁻⁵), raises the disparate impact ratio from 0.88 to 0.99, reduces the demographic-parity difference from 0.068 to 0.007, and attains a perfect score on an operational compliance rubric, while giving up only 0.9 percentage points of accuracy (88.2% versus 89.1%) at roughly 2.6 times the training cost. The results also surface a known tension: enforcing demographic parity increases the equalized-odds gap, quantifying the price of fairness under differential privacy in federated financial systems.