FedPRA: Policy-Residual Aggregation for Heterogeneous Differentially Private Federated Learning.

Federated learning (FL) under heterogeneous privacy policies poses significant challenges. Existing private FL methods assign different privacy budgets to clients, yet only exploit these budgets to control numerical privacy loss. They do not measure whether each trained client model satisfies its own operational policy constraints. In this work, we show that privacy-budget order and policy satisfaction order may diverge. Consequently, conventional data-volume aggregation fails to capture policy satisfaction. To this end, we propose FedPRA to tackle this issue via two complementary designs. First, a differentiable policy engine quantifies each client’s residual constraint violation during local model training with record-level differential privacy. Second, policy-residual aggregation leverages these residuals to guide global aggregation. We prove that the proposed weighting rule prioritizes clients with smaller residuals and preserves a set of theoretical guarantees under disjoint and overlapping client data. Experiments based on two benchmark datasets demonstrate the superiority and competitiveness of FedPRA.