Network Gradient Descent Algorithm for Decentralized Federated Learning

TL;DR

This paper introduces a decentralized federated learning algorithm called network gradient descent (NGD), which enhances privacy and reliability by allowing clients to communicate directly over a network without a central server, with theoretical and numerical validation.

Contribution

The paper proposes a novel NGD algorithm for decentralized federated learning, analyzing its statistical efficiency and demonstrating its effectiveness across various models and deep learning applications.

Findings

NGD can achieve statistical efficiency comparable to centralized methods under certain conditions.

Network structure and learning rate critically influence the estimator's performance.

Numerical experiments validate theoretical results and demonstrate practical applicability.

Abstract

We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed network structure without a central master. This greatly enhances the reliability of the entire algorithm. Those nice properties inspire us to carefully study the NGD method both theoretically and numerically. Theoretically, we start with a classical linear regression model. We find that both the learning rate and the network structure play significant roles in determining the NGD estimator's statistical efficiency. The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is well balanced, even if the data are distributed heterogeneously. Those interesting findings are then extended to general models and loss functions. Extensive numerical studies are presented to corroborate our theoretical findings. Classical deep learning models are also presented for illustration purpose.