Lightweight Federated Learning with Differential Privacy and Straggler
Resilience
Lightweight Federated Learning with Differential Privacy and Straggler
Resilience
Federated learning (FL) enables collaborative model training through model parameter exchanges instead of raw data. To avoid potential inference attacks from exchanged parameters, differential privacy (DP) offers rigorous guarantee against various attacks. However, conventional methods of ensuring DP by adding local noise alone often result in low training accuracy. Combining …