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A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods.

Naghmeh Khajehali1, Jun Yan1, Yang-Wai Chow1

  • 1School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.

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|August 26, 2023
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Summary
This summary is machine-generated.

Federated learning (FL) enables collaborative machine learning (ML) on Internet of Things (IoT) devices. This review details client selection challenges and methods for effective FL in dynamic IoT environments.

Keywords:
client selectiondevice selectionfederated learningmachine learningnode selectionparticipant selection

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Area of Science:

  • * Internet of Things (IoT) and Machine Learning (ML) integration.
  • * Federated Learning (FL) for decentralized data processing.
  • * Optimization of ML models in dynamic, resource-constrained environments.

Background:

  • * Traditional centralized ML faces scalability and privacy issues with vast IoT data.
  • * Federated Learning (FL) trains models collaboratively using parameters, not raw data.
  • * IoT client heterogeneity (computation, communication, network, data quality) poses significant FL challenges.

Purpose of the Study:

  • * To conduct a systematic literature review (SLR) on client selection (CS) challenges in FL.
  • * To provide a comprehensive overview of the CS process and its characteristics for diverse applications.
  • * To categorize and explain existing CS methods for FL in IoT.

Main Methods:

  • * Systematic literature review (SLR) methodology.
  • * Analysis of client selection (CS) process in Federated Learning (FL).
  • * Categorization of CS methods based on characteristics and challenge mitigation.

Main Results:

  • * Identified key challenges in FL client selection due to IoT device heterogeneity and dynamic environments.
  • * Provided a structured overview of the abstract implementation and essential characteristics of CS.
  • * Categorized various CS methods, highlighting their strengths in addressing specific FL challenges.

Conclusions:

  • * Effective client selection is crucial for high-quality federated learning in IoT.
  • * The review offers insights into the current state of CS research in FL.
  • * Provides a roadmap for future research and development of advanced CS methods for FL.