Federated Learning Hyper-Parameter Tuning for Edge Computing

Abstract

Edge computing is widely recognized as a crucial technology for the upcoming generation of communication networks and has garnered significant interest from both industry and academia. Compared to other offloading models like cloud computing, it provides faster data processing capabilities, enhanced security measures, and lower costs by leveraging the proximity of the edge servers to the end devices. This helps mitigate the privacy concerns associated with data transfer in edge computing, by reducing the distance between the data source and the server. Raw data in typical edge computing scenarios still need to be sent to the edge server, leading to data leakage and privacy breaches. Federated Learning (FL) is a distributed model training paradigm that preserves end devices’ data privacy. Therefore, it is crucial to incorporate FL into edge computing to protect data privacy. However, the high training overhead of FL makes it impractical for edge computing. In this study, we propose to facilitate the integration of FL and edge computing by optimizing FL hyper-parameters, which can significantly reduce FL’s training overhead and make it more affordable for edge computing.