A survey of federated learning-based gradient compression for internet of vehicles

Abstract

The Federated Learning (FL) paradigm in the Internet of Vehicles (IoV) leverages vehicular networks and intelligent technologies to integrate data from traffic devices, aiming to develop a smart transportation system with high throughput, low latency, privacy protection, and collaborative multi-party training. However, frequent exchanges of model parameters between numerous vehicle nodes and roadside units (RSUs) in FL lead to uplink channel overload, which poses a significant challenge to system development. To this end, existing works introduce Gradient Compression (GC) technologies, incorporating methods such as count sketch, dynamic adjustment, and sparse matrices. These methods help to reduce communication overhead, enhance transmission efficiency, and maintain FL model training accuracy, making GC a crucial solution for overcoming communication barriers in IoV-based FL systems. In this paper, we first investigate GC technologies, systematically categorizing them by Quantization, Sparsification, and Driving Strategy-orientated compression, followed by using metrics such as efficiency, load, and latency to evaluate them. Secondly, we compare the pros and cons of different compression technologies in addressing communication problems and analyze their key characteristics. Finally, considering the deep integration of IoV and FL, we explore future research directions of the FL framework for IoV, analyze potential challenges, and propose corresponding solutions in conjunction with current mainstream deep learning models.