Federated Learning (FL) is increasingly adopted for training ML models, driven by its ability to preserve data privacy and reduce communication costs. However, the limited availability of wireless bandwidth necessitates efficient client selection and bandwidth allocation. This paper addresses the challenges arising from non-IID data, heterogeneous computing capabilities, and varying communication conditions. We introduce a novel data quality evaluation criterion that comprehensively takes into consideration factors including data size, local data label skew, and the Age of Data. Based on this evaluation criterion, we propose a Joint Efficient Energy-constrained Client Selection and Adaptive Bandwidth Allocation (EECS-Apt) algorithm that leverages data quality, computing capabilities and communication conditions. The experimental results indicate that while satisfying the accuracy requirement, the proposed algorithm can significantly reduce delay by up to 87.4%, 60.7% and 36.8%, respectively, compared to: 1) Joint Energy-constrained Random Client Selection and Average Bandwidth Allocation (ERCS-Avg), 2) Joint Energy-constrained Delay-based Client Selection and Adaptive Bandwidth Allocation (EDCS-Apt), and 3) Reliable and Age-sensitive Client Selection and Adaptive Bandwidth Allocation (RACS-Apt).
Joint Client Selection and Bandwidth Allocation Algorithm for Time-Sensitive Federated Learning over Wireless Networks
2024-06-24
503059 byte
Conference paper
Electronic Resource
English
Elsevier | 2024
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