Federated and Explainable Machine Learning for Secure and Trustworthy SIoT Applications
DOI:
https://doi.org/10.55544/sjmars.4.5.6Keywords:
Federated Learning, Explainable Artificial Intelligence (XAI), Social Internet of Things (SIoT), Data Privacy and Security, Trustworthy Machine LearningAbstract
The Social Internet of Things (SIoT) integrates IoT devices with social networking principles, enabling autonomous device interactions and enhanced user services in applications like smart homes, healthcare, and smart cities. However, SIoT systems face significant challenges in ensuring security, privacy, and trust due to their distributed nature, heterogeneous data, and vulnerability to attacks such as model poisoning and data breaches. This review paper examines the role of federated learning (FL) and explainable artificial intelligence (XAI) in addressing these challenges to build secure and trustworthy SIoT applications. FL enables privacy-preserving, decentralized model training across SIoT devices, while XAI enhances transparency and user trust by providing interpretable model decisions. We synthesize recent research, categorizing approaches based on FL techniques, XAI methods, and SIoT application domains. Key findings highlight the growing adoption of FL for privacy in SIoT, the integration of XAI for transparent decision-making, and persistent gaps, such as scalability issues and limited robustness against adversarial attacks. We identify trends, including the use of differential privacy in FL and post-hoc explanation methods in XAI, and discuss open challenges like balancing explainability with performance and ensuring ethical AI in SIoT. This review provides a comprehensive taxonomy and a roadmap for future research to advance secure and trustworthy SIoT ecosystems.
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