2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Bandwidth-Aware Network Topology Optimization for Decentralized Learning
Yipeng Shen, Zehan Zhu, Yan Huang +3
Network topology is critical for efficient parameter synchronization in distributed learning over networks. However, most existing studies do not account for bandwidth limitations…
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…
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds
Zehan Zhu, Yan Huang, Xin Wang +1
In this paper, we propose a differentially private decentralized learning method (termed PrivSGP-VR) which employs stochastic gradient push with variance reduction and guarantees $…
Robust Fully-Asynchronous Methods for Distributed Training over General Architecture
Zehan Zhu, Ye Tian, Yan Huang +2
Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers. We propose a Rob…
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
Yan Huang, Ying Sun, Zehan Zhu +2
We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenario…