2 papers
cs.LG2025
DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
Zehan Zhu, Heng Zhao, Yan Huang +3
In this paper, we propose a Differentially Private Stochastic Gradient Push with Compressed communication (termed DP-CSGP) for decentralized learning over directed graphs. Differen…
cs.LG2025
Dyn-DP: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
Zehan Zhu, Yan Huang, Xin Wang +2
Most existing decentralized learning methods with differential privacy (DP) guarantee rely on constant gradient clipping bounds and fixed-level DP Gaussian noises for each node thr…