Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression

TL;DR

This paper introduces layered gradient compression (LGC), a novel federated learning framework that efficiently utilizes multiple communication channels by encoding gradients into layers, reducing resource consumption while maintaining model accuracy.

Contribution

The paper proposes LGC, a layered gradient encoding scheme for federated learning over multi-channel networks, with a dynamic algorithm for resource-efficient training and proven convergence.

Findings

LGC reduces training time significantly.

LGC improves resource utilization.

LGC maintains comparable accuracy to existing methods.

Abstract

A fundamental issue for federated learning (FL) is how to achieve optimal model performance under highly dynamic communication environments. This issue can be alleviated by the fact that modern edge devices usually can connect to the edge FL server via multiple communication channels (e.g., 4G, LTE and 5G). However, having an edge device send copies of local models to the FL server along multiple channels is redundant, time-consuming, and would waste resources (e.g., bandwidth, battery life and monetary cost). In this paper, motivated by the layered coding techniques in video streaming, we propose a novel FL framework called layered gradient compression (LGC). Specifically, in LGC, local gradients from a device is coded into several layers and each layer is sent to the FL server along a different channel. The FL server aggregates the received layers of local gradients from devices to update the global model, and sends the result back to the devices. We prove the convergence of LGC, and formally define the problem of resource-efficient federated learning with LGC. We then propose a learning based algorithm for each device to dynamically adjust its local computation (i.e., the number of local stochastic descent) and communication decisions (i.e.,the compression level of different layers and the layer to channel mapping) in each iteration. Results from extensive experiments show that using our algorithm, LGC significantly reduces the training time, improves the resource utilization, while achieving a similar accuracy, compared with well-known FL mechanisms.