首页|A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

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  • NETL
  • NSTL
  • Assoc Computing Machinery
Federated Learning (FL) has gained attention across various industries for its capability to train machine learningmodels without centralizing sensitive data. While this approach offers significant benefits such as privacypreservation and decreased communication overhead, it presents several challenges, including deploymentcomplexity and interoperability issues, particularly in heterogeneous scenarios or resource-constrained environments.Over-the-air (OTA) FL was introduced to tackle these challenges by disseminating model updateswithout necessitating direct device-to-device connections or centralized servers. However, OTA-FL broughtforth limitations associated with heightened energy consumption and network latency. In this article, wepropose a multi-attribute client selection framework employing the grey wolf optimizer (GWO) to strategicallycontrol the number of participants in each round and optimize the OTA-FL process while consideringaccuracy, energy, delay, reliability, and fairness constraints of participating devices. We evaluate the performanceof our multi-attribute client selection approach in terms of model loss minimization, convergence timereduction, and energy efficiency. In our experimental evaluation, we assessed and compared the performanceof our approach against the existing state-of-the-art methods. Our results demonstrate that the proposedGWO-based client selection outperforms these baselines across various metrics. Specifically, our approachachieves a notable reduction in model loss, accelerates convergence time, and enhances energy efficiencywhile maintaining high fairness and reliability indicators.

Over-the-air federated learningclient selectiongrey wolf optimizerconvergence speedenergy efficiencyreliabilityfairness

MARYAM BEN DRISS、ESSAID SABIR、HALIMA ELBIAZE、ABDOULAYE DIALLO、MOHAMED SADIK

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Informatique, Universite du Quebec a Montreal, Montreal, Canada and NESTResearch group, LRI, Universite Hassan Ⅱ de Casablanca, Casablanca, Morocco

Science and technology, TELUQ, University of Quebec, Montreal, Canada

Universite du Quebec a Montreal, Montreal, Canada

Universite Hassan II de Casablanca, Casablanca, Morocco

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2025

10.1145/3706059

ACM Transactions on Modeling and Performance Evaluation of Computing Systems

ACM Transactions on Modeling and Performance Evaluation of Computing Systems

ISSN：2376-3639
年,卷(期)：2025.10(1)
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