Understanding Differential Privacy in Decentralized Federated Learning: A Controlled Privacy–Utility Comparison
TL;DR
Results show that clipping, perturbation, and the learning procedure jointly influence the observed privacy–utility behavior of centralized FL and sequential Decentralized Federated Learning.
Abstract
Centralized Federated Learning (FL) enables collaborative model training without sharing raw data. Differential privacy (DP) is widely used to protect sensitive information in FL; however, its behavior in decentralized environments remains poorly understood. This study empirically compares centralized FL and sequential Decentralized Federated Learning (DFL) under matched clipping and perturbation settings to examine model utility and privacy leakage. Both frameworks were evaluated under a common experimental setup with non-IID data, and each configuration was evaluated across five independent seeds. Utility was evaluated under matched experimental perturbation parameters, whereas formal client-level privacy accounting was applied to the perturbed round-end model releases, with each client’s complete dataset treated as the protected unit. Empirical leakage was evaluated separately using membership inference and gradient inversion attacks. Within the evaluated MNIST configuration, the results show that clipping, perturbation, and the learning procedure jointly influence the observed privacy–utility behavior. At C=1 and ϵcal=2, the mean final accuracy was 73.83% for FL and 97.74% for sequential DFL. At ϵcal=4, InvGrad reconstruction for FL produced an MSE of 0.048, PSNR of 13.71, and SSIM of 0.280, compared with 0.130, 10.10, and 0.184 for sequential DFL, respectively. Future research will investigate topology-aware privacy mechanisms and adaptive noise allocation for decentralized systems.