Efficient Intrusion Detection and Trust Management in Federated Learning Systems

This paper proposes a novel Multi-Stage Anomaly Detection System (MSADS) to combat Fake Traffic Injection (FTI) attacks in federated learning (FL). The MSADS incorporates data normalization and statistical thresholding to establish baseline performance profiles for model updates. Anomaly detection is enhanced through the use of clustering algorithms, which group data points based on similarities. Local Outlier Factor (LOF) assessments further identify deviations that may suggest malicious activity. Additionally, a Reputation-Based Trust Framework (RBTF) is developed, assigning dynamic reputation scores to clients based on their update quality, thus fostering accountability and peer review mechanisms. Finally, Federated Defense Mechanisms (FDM) leverage the decentralized structure of FL to implement strategies like model diversity, homomorphic encryption, and decentralized aggregation, reducing the risks associated with FTI. By integrating these methods, the proposed approach strengthens the security of FL systems while ensuring robust model performance and privacy. This research highlights the critical need for proactive measures in securing federated learning against sophisticated threats, ensuring a collaborative environment where clients are motivated to contribute authentically. The findings emphasize the importance of adaptive security protocols in the evolving landscape of distributed machine learning.