Adaptive Differential Privacy Noise Injection for Decentralized Federated Learning of Visual Recognition Tasks

With the growing volume of Internet of Things (IoT) data acquired in edge-side visual recognition applications, peer-to-peer decentralized federated learning (DFL) has emerged as an attractive model training paradigm. Unlike centralized FL where a server aggregates per-round client updates, DFL clients directly exchange sparsified model parameters (only a subset is transmitted each round). The exchanged parameters embed accumulated training history rather than independent round updates. This creates a fundamental dilemma for privacy protection with differential privacy (DP): to prevent historical leakage, existing methods are forced to update only the small fraction (e.g., 10%) of parameters currently being exchanged and protected by DP noise, leaving most parameters frozen and severely degrading accuracy. In this paper, we propose DecentDP, an approach that addresses this dilemma to improve model accuracy. The two key modules of DecentDP are: (i) a parameter selector that designates a subset of parameters for local-only updates, allowing them to be updated without DP noise; and (ii) a noise injector that adaptively adds less DP noise to exchangeable parameters that are more sensitive to DP noise. Experiments across five settings show that DecentDP outperforms 11 baselines, achieving an average accuracy improvement of 15.68%, with only 0.70% additional computational overhead and 0.60% additional communication overhead.