Distributed Machine Learning for Cyber Defense in IoT Environments - A Systematic Literature Review

Sammanfattning

The Internet of Things (IoT) has become a critical technology across diverse industries, but its rapid expansion has brought significant security challenges. Centralized security mechanisms often struggle to address the evolving threat landscape. Distributed machine learning (DML) has emerged as an effective approach to strengthen IoT cyber defense, but a comprehensive review of its application and effectiveness is still lacking. Therefore, this thesis investigates how DML can be effectively designed and implemented to enhance IoT security and addresses how such designs can improve privacy, communication efficiency, and robustness against adversarial attacks. A systematic literature review (SLR) was conducted to explore existing DML applications for IoT security. Thematic analysis of 34 selected studies identified six key themes, including IoT and DML attacks, security requirements, technologies and approaches, privacy protection, communication efficiency, and robustness. Based on these findings, a four-layer conceptual framework was developed, comprising Threat Landscape, Core and Functional Requirements, Technologies and Approaches, and IoT Security. Supported by nine key evaluation metrics for assessing DML system performance, this framework provides structured guidance for designing DML-based security solutions in IoT environments. In addition, three open issues were identified in the literature: the trustworthiness of DML predictions, the heterogeneity management in DML, and the trade-offs between privacy, scalability, and performance. By highlighting the critical challenges and future research directions, this thesis contributes to a deeper understanding of the current state and potential of DML for IoT cyber defense.