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cs.LG2022
SAGDA: Achieving Communication Complexity in Federated Min-Max Learning
Haibo Yang, Zhuqing Liu, Xin Zhang +1
To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as…
cs.LG2022
SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters
Zhuqing Liu, Xin Zhang, Jia Liu
To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed sto…
cs.LG2022
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Xin Zhang, Minghong Fang, Zhuqing Liu +3
Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. A…