Title: On The Surprising Effectiveness of a Single Global Merging in Decentralized Learning
Primary area: optimization
Keywords: Decentralized Learning, Model Merging

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 to improve global generalization, 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 generalization.
Surprisingly, we uncover that fully connected communication at the final step, implemented by a single global merging, can significant improve the generalization performance of decentralized learning under serve high data heterogeneity. 
Our theoretical contributions, which explains these phenomena, are 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 promising results that  decentralized learning is able to generalize under high data heterogeneity and limited communication, while offering broad new avenues for model merging research. 
The code will be made publicly available.

TL;DR: We discover and theoretically explain why and when a single global parameter merging in decentralized learning can recover the performance of federated learning, even in highly heterogeneous and communication-constrained environments.