Learning in the Right Subspace: Personalized Differential Private Federated Learning with Noise Filtering

Abstract

Differential privacy (DP) mechanisms have been widely adopted in federated learning (FL) to enhance model security. However, existing approaches predominantly employ uniform privacy budgets, neglecting personalized requirements arising from heterogeneous user privacy preferences. Such uniform privacy configurations typically necessitate compliance with the most stringent budget, which not only leads to the wasteful underutilization of privacy budgets for certain clients but also compromises overall model utility. To address this limitation, we propose FedSPA, a Subspace Projection Aggregation personalized differential private Federated learning framework. The proposed method conducts singular value decomposition operations on noise-perturbed local models to extract singular value vectors as compact representations of both model structure and privacy noise. The server then clusters clients and identifies a consensus subspace for projecting models with varying noise levels, ultimately aggregating the global model through a residual-aware mechanism. This method not only effectively guides the aggregation of client personalized differential privacy but also reduces communication overhead. Extensive experiments demonstrate the model's effectiveness. Additionally, we provide theoretical proof of the privacy and convergence of FedSPA. Experimental results also showcase its superior performance over personalized DP-FL baselines.