Sensors, Vol. 24, Pages 2476: Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments
In this study, we propose a resource-efficient FL scheme. We follow the Pareto optimality concept with the biased client selection to limit client participation, thereby ensuring efficient resource consumption and rapid model convergence. In addition, we propose a hierarchical structure with location-based clustering for device-to-device communication using k-means clustering. Simulation results show that with prate at 0.75, the proposed scheme effectively reduced transmitted and received network traffic by 75.89% and 78.77%, respectively, compared to the FedAvg method. It also achieves faster model convergence compared to other FL mechanisms, such as FedAvg and D2D-FedAvg.
Source: Sensors - Category: Biotechnology Authors: June-Pyo Jung Young-Bae Ko Sung-Hwa Lim Tags: Article Source Type: research
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