Adaptive Client Selection with Personalization for Communication Efficient Federated Learning

TL;DR

This paper presents ACSP-FL, a federated learning method that adaptively selects clients and personalizes models, significantly reducing communication costs and improving convergence in distributed environments.

Contribution

The paper introduces ACSP-FL, a novel client selection and personalization strategy that minimizes communication overhead and enhances model performance in federated learning.

Findings

Reduces communication costs by up to 95% compared to existing methods.

Achieves efficient convergence even with non-i.i.d. data distributions.

Improves client model performance through personalization.

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.