3 papers
cs.LG2025
ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push
Xiaoming Wu, Teng Liu, Xin Wang +2
Differential privacy is widely employed in decentralized learning to safeguard sensitive data by introducing noise into model updates. However, existing approaches that use fixed-v…
cs.CR2025
PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks
Hongliang Zhang, Jiguo Yu, Fenghua Xu +5
Privacy-Preserving Federated Learning (PPFL) enables multiple clients to collaboratively train models by submitting secreted model updates. Nonetheless, PPFL is vulnerable to data…
cs.CR2025
Differentially Private Distance Query with Asymmetric Noise
Weihong Sheng, Jiajun Chen, Chunqiang Hu +3
With the growth of online social services, social information graphs are becoming increasingly complex. Privacy issues related to analyzing or publishing on social graphs are also…