Role of federated learning in edge computing: A survey

Abstract

This paper explores various approaches to enhance federated learning (FL) through the utilization of edge computing. Three techniques, namely Edge-Fed, hybrid federated learning at edge devices, and cluster federated learning, are investigated. The Edge-Fed approach implements the computational and communication challenges faced by mobile devices in FL by offloading calculations to edge servers. It introduces a network architecture comprising a central cloud server, an edge server, and IoT devices, enabling local aggregations and reducing global communication frequency. Edge-Fed offers benefits such as reduced computational costs, faster training, and decreased bandwidth requirements. Hybrid federated learning at edge devices aims to optimize FL in multi-access edge computing (MAEC) systems. Cluster federated learning introduces a cluster-based hierarchical aggregation system to enhance FL performance. The paper explores the applications of these techniques in various domains, including smart cities, vehicular networks, healthcare, cybersecurity, natural language processing, autonomous vehicles and smart homes. The combination of edge computing (EC) and federated learning (FL) is a promising technique gaining popularity across many applications. EC brings cloud computing services closer to data sources, further enhancing FL. The integration of FL and EC offers potential benefits in terms of collaborative learning.