FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning

TL;DR

This paper introduces FedCGD, a novel device scheduling algorithm for wireless federated learning that optimizes collective gradient divergence to improve convergence speed and classification accuracy while reducing device participation.

Contribution

It proves the impact of collective gradient divergence on FL convergence, models it using weighted earth moving distance, and proposes an algorithm to balance divergence and sampling variance.

Findings

Increases CIFAR-10 accuracy by up to 4.2%.

Schedules 41.8% fewer devices.

Flexibly balances WEMD reduction and sampling variance.

Abstract

Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performance of FL, i.e., data heterogeneity of devices and limited bandwidth. Many papers have investigated device scheduling strategies considering the two issues. However, most of them recognize data heterogeneity as a property of individual devices. In this paper, we prove that the convergence speed of FL is affected by the sum of device-level and sample-level collective gradient divergence (CGD). The device-level CGD refers to the gradient divergence of the scheduled device group, instead of the sum of the individual device divergence. The sample-level CGD is statistically upper bounded by sampling variance, which is inversely proportional to the total number of samples scheduled for local update. To derive a tractable form of the device-level CGD, we further consider a classification problem and transform it into the weighted earth moving distance (WEMD) between the group distribution and the global distribution. Then we propose FedCGD algorithm to minimize the sum of multi-level CGDs by balancing WEMD and sampling variance, within polynomial time. Simulation shows that the proposed strategy increases classification accuracy on the CIFAR-10 dataset by up to 4.2\% while scheduling 41.8\% fewer devices, and flexibly switches between reducing WEMD and reducing sampling variance.