873 citations · 1.8k across the 33 of their papers we have counts for
35 papers
Byzantine-Robust Decentralized Stochastic Optimization with Stochastic Gradient Noise-Independent Learning Error
Jie Peng, Weiyu Li, Qing Ling
This paper studies Byzantine-robust stochastic optimization over a decentralized network, where every agent periodically communicates with its neighbors to exchange local models, a…
A Communication-Efficient Decentralized Newton's Method with Provably Faster Convergence
Huikang Liu, Jiaojiao Zhang, Anthony Man-Cho So +1
In this paper, we consider a strongly convex finite-sum minimization problem over a decentralized network and propose a communication-efficient decentralized Newton's method for so…
Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rules
Zhaoxian Wu, Tianyi Chen, Qing Ling
This paper focuses on decentralized stochastic optimization in the presence of Byzantine attacks. During the optimization process, an unknown number of malfunctioning or malicious…
Confederated Learning: Federated Learning with Decentralized Edge Servers
Bin Wang, Jun Fang, Hongbin Li +2
Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider…
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part II
Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So +1
In Part I of this work, we have proposed a general framework of decentralized stochastic quasi-Newton methods, which converge linearly to the optimal solution under the assumption…
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part I
Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So +1
In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of cost functions over a decentralized network. In Part I, we develop a general algorithmic…