Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data

arXiv:2403.03292v1 Announce Type: new
Abstract: State-of-the-art decentralized learning algorithms typically require the data distribution to be Independent and Identically Distributed (IID). However, in practical scenarios, the data distribution across the agents can have significant heterogeneity. In this work, we propose averaging rate scheduling as a simple yet effective way to reduce the impact of heterogeneity in decentralized learning. Our experiments illustrate the superiority of the proposed method (~3% improvement in test accuracy) compared to the conventional approach of employing a …

abstract agents algorithms art arxiv cs.dc cs.lg data decentralized distributed distribution however independent practical rate reduce scheduling simple state type work