Adaptive Client Selection with Personalization for Communication Efficient Federated Learning

• URL: http://arxiv.org/abs/2411.17833v1
• Date: Tue, 26 Nov 2024 19:20:59 GMT
• Title: Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
• Authors: Allan M. de Souza, Filipe Maciel, Joahannes B. D. da Costa, Luiz F. Bittencourt, Eduardo Cerqueira, Antonio A. F. Loureiro, Leandro A. Villas,
• Abstract summary: Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. This article introduces ACSP-FL, a solution to reduce the overall communication and computation costs for training a model in FL environments.
• Score: 2.8484833657472644
• License:
• 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.

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