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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.
Sensors (Basel, Switzerland)
|August 26, 2023
Summary
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.
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.
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