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A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods by Naghmeh Khajehali; Jun Yan; Yang-Wai Chow; Mahdi Fahmideh is a Engineering article available to read on EtoBox.

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The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the candidates. This study presents a comprehensive systematic literature review (SLR) that focuses on the challenges of client selection (CS) in the context of federated learning (FL). The objective of t

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Author

Naghmeh Khajehali; Jun Yan; Yang-Wai Chow; Mahdi Fahmideh

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)