7 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…
Beyond Scaffold: A Unified Spatio-Temporal Gradient Tracking Method
Yan Huang, Jinming Xu, Jiming Chen +1
In distributed and federated learning algorithms, communication overhead is often reduced by performing multiple local updates between communication rounds. However, due to data he…
An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
Yan Huang, Jinming Xu, Jiming Chen +1
This paper studies the distributed minimax optimization problem over networks. To enhance convergence performance, we propose a distributed optimistic gradient tracking method, ter…
CoCoL: A Communication Efficient Decentralized Collaborative Method for Multi-Robot Systems
Jiaxi Huang, Yan Huang, Yixian Zhao +2
Collaborative learning enhances the performance and adaptability of multi-robot systems in complex tasks but faces significant challenges due to high communication overhead and dat…
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…