Federated Learning

Robust Personalized Federated Learning with Sparse Penalization

Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the …

Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang

NET-FLEET: Achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data

Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient …

Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, Zhengyuan Zhu

SAGDA: Achieving O(ε−2) Communication Complexity in Federated Min-Max Learning

Federated min-max learning has received increasing attention in recent years thanks to its wide range of applications in various learning paradigms. In this paper, we propose a new algorithmic framework called stochastic sampling averaging gradient descent ascent (SAGDA), which yields an O(ε−2) communication complexity that is orders of magnitude lower than the state of the art.

Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu

Anarchic federated learning

Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of …

Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu