Local Methods with Adaptivity via Scaling

TL;DR

This paper proposes a unified framework combining local training with adaptive scaling methods like Adam to improve distributed learning efficiency, supported by theoretical analysis and practical neural network training results.

Contribution

It introduces a generic scaling enhancement to Local SGD, enabling unified analysis of adaptive methods in distributed training.

Findings

Enhanced Local SGD with scaling improves convergence.

Unified analysis applies to Adam, RMSProp, OASIS.

Practical validation shows performance gains.

Abstract

The rapid development of machine learning and deep learning has introduced increasingly complex optimization challenges that must be addressed. Indeed, training modern, advanced models has become difficult to implement without leveraging multiple computing nodes in a distributed environment. Distributed optimization is also fundamental to emerging fields such as federated learning. Specifically, there is a need to organize the training process to minimize the time lost due to communication. A widely used and extensively researched technique to mitigate the communication bottleneck involves performing local training before communication. This approach is the focus of our paper. Concurrently, adaptive methods that incorporate scaling, notably led by Adam, have gained significant popularity in recent years. Therefore, this paper aims to merge the local training technique with the adaptive approach to develop efficient distributed learning methods. We consider the classical Local SGD method and enhance it with a scaling feature. A crucial aspect is that the scaling is described generically, allowing us to analyze various approaches, including Adam, RMSProp, and OASIS, in a unified manner. In addition to theoretical analysis, we validate the performance of our methods in practice by training a neural network.