Defense Against Information Integrity Attacks in Federated IoT Systems Using Inertial Momentum-Aware IALM-RPCA

While Federated Learning offers a decentralized approach to model training, ensuring the integrity of the information from each IoT client remains a challenge. This work delves into the dynamics of Multi-Stage Federated Learning, its susceptibility to information integrity attacks, and how to defend against such threats. A comprehensive understanding of data uncertainty and the challenges of poisoning attacks is discussed, laying a solid groundwork for the proposed defense mechanisms. At its core, this paper introduces a novel Multi-Stage Federated Learning model that segments the Federated Learning process into distinct phases with a novel approach of inertial momentum aware Inexact Augmented Lagrange Multiplier Robust PCA with constant momentum factor and unaltered norm of the traditional one, each tailored to optimize for both efficiency and security. This robust framework is then tested against data injection based poisoning attacks, using sparse noise, and demonstrates the effectiveness of the proposed recovery techniques like Robust PCA. Performance results highlight the resilience and efficacy of the introduced model with novel reconstruction algorithm, emphasizing the importance of this approach in real-world IoT settings. Data analysis, model summaries, and impacts of adversarial attacks further reinforce the findings, which are evaluated using rigorous statistical metrics and machine learning algorithms. The paper concludes by acknowledging its efficacy in detection and recovery from data poisoning attacks, improving robustness and data reconstruction in IoT environments while highlighting opportunities for further security enhancements.