Private and Communication-Efficient Federated Learning based on Differentially Private Sketches

Federated learning (FL) faces two primary challenges: the risk of privacy leakage due to parameter sharing and communication inefficiencies. To address these challenges, we propose DPSFL, a federated learning method that utilizes differentially private sketches. DPSFL compresses the local gradients of each client using a count sketch, thereby improving communication efficiency, while adding noise to the sketches to ensure differential privacy (DP). We provide a theoretical analysis of privacy and convergence for the proposed method. Gradient clipping is essential in DP learning to limit sensitivity and constrain the addition of noise. However, clipping introduces bias into the gradients, negatively impacting FL performance. To mitigate the impact of clipping, we propose an enhanced method, DPSFL-AC, which employs an adaptive clipping strategy. Experimental comparisons with existing techniques demonstrate the superiority of our methods concerning privacy preservation, communication efficiency, and model accuracy.