Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

Abstract

We envision a mobile edge computing (MEC) framework for machine learning (ML)\ntechnologies, which leverages distributed client data and computation resources\nfor training high-performance ML models while preserving client privacy. Toward\nthis future goal, this work aims to extend Federated Learning (FL), a\ndecentralized learning framework that enables privacy-preserving training of\nmodels, to work with heterogeneous clients in a practical cellular network. The\nFL protocol iteratively asks random clients to download a trainable model from\na server, update it with own data, and upload the updated model to the server,\nwhile asking the server to aggregate multiple client updates to further improve\nthe model. While clients in this protocol are free from disclosing own private\ndata, the overall training process can become inefficient when some clients are\nwith limited computational resources (i.e. requiring longer update time) or\nunder poor wireless channel conditions (longer upload time). Our new FL\nprotocol, which we refer to as FedCS, mitigates this problem and performs FL\nefficiently while actively managing clients based on their resource conditions.\nSpecifically, FedCS solves a client selection problem with resource\nconstraints, which allows the server to aggregate as many client updates as\npossible and to accelerate performance improvement in ML models. We conducted\nan experimental evaluation using publicly-available large-scale image datasets\nto train deep neural networks on MEC environment simulations. The experimental\nresults show that FedCS is able to complete its training process in a\nsignificantly shorter time compared to the original FL protocol.\n

Affiliated Institutions

• Kyoto University JP
• Omron (Japan) JP

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