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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…
cs.LG2023
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
Yuecheng Li, Jialong Chen, Chuan Chen +2
Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods ha…