Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization

Mathieu Even

Abstract:

Decentralized and asynchronous communications are two popular techniques to speedup
communication complexity of distributed
machine learning, by respectively removing
the dependency over a central orchestrator
and the need for synchronization. Yet, combining these two techniques together still remains a challenge. In this paper, we take
a step in this direction and introduce Asynchronous SGD on Graphs (AGRAF SGD) —
a general algorithmic framework that covers asynchronous versions of many popular algorithms including SGD, Decentralized
SGD, Local SGD, FedBuff, thanks to its relaxed communication and computation assumptions. We provide rates of convergence
under much milder assumptions than previous decentralized asynchronous works, while
still recovering or even improving over the
best know results for all the algorithms covered.