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 remarkably improves global test performance. Surprisingly, we uncover that fully connected communication at the final step, implemented by a single global merging, 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 were previously considered as detrimental noise, as constructive components essential for matching this rate. This work provides evidence that decentralized learning is able to generalize under high data heterogeneity and limited communication, while offering broad new avenues for model merging research.

翻译：去中心化学习提供了参数服务器训练的可扩展替代方案，但其性能常受限于有限的点对点通信。本文研究如何调度通信时间，包括确定设备同步的时机与频率。反直觉的实验结果表明，将通信预算集中在去中心化训练后期可显著提升全局测试性能。令人惊讶的是，我们发现最终步骤通过单次全局合并实现的全连接通信，能在数据高度异构情况下显著改善去中心化学习的性能。为解释这些现象，我们的理论贡献首次证明：去中心化SGD的全局合并模型能够达到并行SGD的收敛速率。技术上，我们将局部模型间的部分差异（此前被视为有害噪声）重新解读为匹配该速率所必需的建设性成分。这项工作证明去中心化学习能在高数据异构性和有限通信条件下实现泛化，同时为模型合并研究开辟了广阔新路径。