Get More for Less in Decentralized Learning Systems
Akash Dhasade,
Anne-Marie Kermarrec,
Rafael Pires
et al.
Abstract:Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality. However, the large size of deep neural networks poses a significant challenge for decentralized training, since each node needs to exchange gigabytes of data, overloading the network. In this paper, we address this challenge with JWINS, a communication-efficient and fully decentralized learning system that shares only a subset of par… Show more
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