Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy consideratio...

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Bibliographic Details
Main Authors: Dan Ben Ami, Kobi Cohen, Qing Zhao
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning (FL)
client selection
client scheduling
multi-armed bandit (MAB)
generalization in machine learning
Online Access:https://ieeexplore.ieee.org/document/10891761/
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