Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning

Authors: Maryam Ben Driss and Essaid Sabi and Halima Elbiaze and Abdoulaye Baniré Diallo

Date: 2025-12-01

Status: Published

DOI: 10.1109/GLOBECOM59602.2025.11431785

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Over-the-air federated learning (OTA-FL) is a communication-efficient paradigm that leverages the superposition property of wireless channels to aggregate client updates simultaneously, significantly reducing uplink latency and bandwidth usage. While OTA-FL offers advantages in scalability and speed, it poses challenges in energy efficiency and delay management. This paper proposes a multi-attribute client selection framework that addresses these challenges through a multi-objective optimization approach. We analytically model selection attributes: energy efficiency, communication delay, loss, and fairness, and formulate three optimization problems to capture different trade-offs. To solve them, we employ the Multi-Objective Grey Wolf Optimizer (MOGWO), a nature-inspired metaheuristic algorithm that effectively balances exploration and exploitation. Experiments on MNIST, Fashion MNIST, and CIFAR-10 demonstrate that our approach outperforms baseline and loss-aware methods, achieving up to 13% energy savings while improving model accuracy, fairness, and reliability.