Federated Learning for All: A Reinforcement Learning-Based Approach for Ensuring Fairness in Client Selection

Federated Learning for All: A Reinforcement Learning-Based Approach for Ensuring Fairness in Client Selection

In federated learning, selecting participating devices (clients) is critical due to their inherent diversity. Clients typically hold non-IID data and possess varying computational and communication capabilities, which introduces heterogeneity that can impact overall system performance. Ignoring this...

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Bibliographic Details
Main Authors: Saeedeh Ghazi, Saeed Farzi, Amirhossein Nikoofard
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Client selection
fairness
federated learning
reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/11072670/
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https://ieeexplore.ieee.org/document/11072670/

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