Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection

22 Feb 2020Zhenheng TangShaohuai ShiXiaowen Chu

Distributed learning techniques such as federated learning have enabled multiple workers to train machine learning models together to reduce the overall training time. However, current distributed training algorithms (centralized or decentralized) suffer from the communication bottleneck on multiple low-bandwidth workers (also on the server under the centralized architecture)... (read more)

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