FedDecouple: Mitigating Noise Accumulation in Differentially Private Federated Learning via Phase Decoupling

Abstract

Differential privacy protection in federated learning faces the fundamental challenge of noise accumulation: noise added dispersedly by clients accumulates in variance during server-side aggregation, severely harming model convergence and accuracy. This paper proposes FedDecouple, a phase-decoupled differentially private federated learning framework that is analytically suited for resource-constrained mobile devices. The core innovation lies in decoupling the noise addition phase from the client computation phase—clients only upload clean gradients, while two auxiliary servers collaboratively generate and inject noise through a secure two-party MPC protocol. This design reduces the effective noise variance while eliminating the per-sample gradient computation burden on clients. Experimental results show that on MNIST, FedDecouple maintains 97.75% accuracy under strict privacy, significantly outperforming client-side noised DP-SGD with 94.0% accuracy. On CIFAR-10, it achieves 76.2% test accuracy, which is 13.4 percentage points higher than DP-SGD. FedDecouple’s total training time on both datasets is faster than Opacus and DP-SGD.