Privacy-Preserving Robust Federated Learning Based on Threshold Homomorphic Encryption
Federated learning (FL) enables collaborative model training without sharing raw data, but remains vulnerable to Byzantine attacks and privacy leakage. Existing privacy-preserving robust FL schemes suffer from prohibitive computation and communication overheads, particularly on resourceconstrained clients. To address t...