On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning

Abstract

Decentralized learning provides a scalable alternative to parameter-server-based training, yet its performance is often hindered by limited peer-to-peer communication. In this paper, we study how communication should be scheduled over time, including determining when and how frequently devices synchronize. Counterintuitive empirical results show that concentrating communication budgets in the later stages of decentralized training substantially improves global test performance. Surprisingly, we find that fully connected communication at the final step, implemented as a single global merge, can significantly improve the performance of decentralized learning under high data heterogeneity. Our theoretical contributions, which explain these phenomena, are the first to establish that the globally merged model of decentralized SGD can match the convergence rate of parallel SGD. Technically, we reinterpret part of the discrepancy among local models, which was previously considered detrimental noise, as a constructive component essential for matching this rate. This work provides evidence that decentralized learning can generalize under high data heterogeneity and limited communication, while offering broad new avenues for model merging research.