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cs.LG2025
ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
Zihou Wu, Yuecheng Li, Tianchi Liao +2
Federated learning (FL) faces a critical dilemma: existing protection mechanisms like differential privacy (DP) and homomorphic encryption (HE) enforce a rigid trade-off, forcing a…
cs.LG2024★ 3 cited
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li, Lele Fu, Tong Wang +6
To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic…