Client Selection in Federated Learning on Resource-Constrained Devices: A Game Theory Approach

Client Selection in Federated Learning on Resource-Constrained Devices: A Game Theory Approach

Federated Learning (FL), a key paradigm in privacy-preserving and distributed machine learning (ML), enables collaborative model training across decentralized data sources without requiring raw data exchange. FL enables collaborative model training across decentralized data sources while preserving...

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Bibliographic Details
Main Authors: Zohra Dakhia, Massimo Merenda
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
federated learning
client selection
game theory
cooperative learning
heterogeneous devices
reward-based payoff strategy
Online Access:https://www.mdpi.com/2076-3417/15/13/7556
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