Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

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

I. Introduction

A variety of modern AI products are powered by cutting-edge machine learning (ML) technologies , which range from face detection and language translation installed on smart-phones to voice recognition and speech synthesis used in virtual assistants such as Amazon Alexa and Google Home. Therefore, the development of such AI products typically necessitates large-scale data, which are essential for training high-performance ML models such as a deep neural network. Arguably, a massive amount of IoT devices, smartphones, and autonomous vehicles with high-resolution sensors, all of which are connected to a high-speed network, can serve as promising data collection infrastructure in the near future (e.g., [1]). Researchers in the field of communication and mobile computing have started to interact with data science communities in the last decade and have proposed mobile edge computing (MEC) frameworks that can be used for large-scale data collection and processing [2].