Advancements in securing federated learning with IDS: a comprehensive review of neural networks and feature engineering techniques for malicious client detection
Abstract
Federated Learning (FL) is a technique that can learn a global machine-learning model at a central server by aggregating locally trained models. This distributed machine-learning approach preserves the privacy of local models. However, FL systems are inherently vulnerable to significant security challenges such as cyber-attacks, handling non-independent and identically distributed (non-IID) data, and data privacy concerns. This systematic literature review addresses these issues by examining advanced neural network models, feature engineering methods, and privacy-preserving techniques within intrusion detection systems (IDS) for FL environments. These are key elements for improving the security of FL systems. To the best of our knowledge, this review is among the first to comprehensively explore the combined impacts of these technologies. We analyzed 88 studies published between 2021 and October 2024. This study offers valuable insights for future research directions, including scaling FL in a real-world environment.
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