<ul data-eligibleForWebStory="false"><li>Decentralized learning is seen as a scalable alternative to traditional parameter-server-based training, but faces challenges due to limited peer-to-peer communication.</li><li>Researchers studied how communication is scheduled in decentralized learning and found that concentrating communication in later stages improves global generalization.</li><li>The study revealed that fully connected communication with a single global merging at the final step can match the performance of server-based training.</li><li>Theoretical contributions of the research show that globally merged decentralized SGD can converge faster than centralized mini-batch SGD, challenging common beliefs about decentralized learning.</li></ul>

A Single Merging Suffices: Recovering Server-based Learning Performance in Decentralized Learning

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