Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
Abstract
Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. FL requires a high level of communication between the devices and a central server, thus imposing several challenges, including communication bottlenecks and network scalability. This article introduces ACSP-FL (https://github.com/AllanMSouza/ACSP-FL), a solution to reduce the overall communication and computation costs for training a model in FL environments. ACSP-FL employs a client selection strategy that dynamically adapts the number of devices training the model and the number of rounds required to achieve convergence. Moreover, ACSP-FL enables model personalization to improve clients performance. A use case based on human activity recognition datasets aims to show the impact and benefits of ACSP-FL when compared to state-of-the-art approaches. Experimental evaluations show that ACSP-FL minimizes the overall communication and computation overheads to train a model and converges the system efficiently. In particular, ACSP-FL reduces communication up to 95% compared to literature approaches while providing good convergence even in scenarios where data is distributed differently, non-independent and identical way between client devices.
Cite
@article{arxiv.2411.17833,
title = {Adaptive Client Selection with Personalization for Communication Efficient Federated Learning},
author = {Allan M. de Souza and Filipe Maciel and Joahannes B. D. da Costa and Luiz F. Bittencourt and Eduardo Cerqueira and Antonio A. F. Loureiro and Leandro A. Villas},
journal= {arXiv preprint arXiv:2411.17833},
year = {2024}
}