SAFE-IDS: A privacy-preserving framework for overcoming non-IID challenges in federated intrusion detection
Alimov Abdulboriy Abdulkhay Ugli, Ji Sun Shin · Computers & Security · 2025
Federated learning has advanced intrusion detection systems (IDS) by enabling collaborative model training without requiring direct data sharing. This approach allows multiple institutions to contribute to and benefit from a shared model, enhancing detection capabilities. Despite these advances, the security of model updates remains a significant concern, as malicious actors may reverse-engineer the underlying data from these updates. Additionally, existing federated learning techniques struggle with non-IID (non-Independent and Identically Distributed) data distributions and are vulnerable to inference attacks on model updates. For example, methods like SignSGD , while providing some privacy benefits through gradient sign manipulation, suffer from accuracy degradation, especially when dealing with non-IID data. Similarly, FedAvg , while effective in handling non-IID data, is prone to privacy breaches as it transmits full model updates, potentially revealing sensitive information. To address these challenges, we propose SAFE-IDS , a novel framework combining gradient sign-based aggregation with the zSignFedAvg optimizer. Unlike SignSGD , it incorporates a unified learning rate and weighted loss function to mitigate accuracy loss in non-IID settings. Additionally, while FedAvg shares full model updates, SAFE-IDS only shares gradient signs, enhancing privacy. The integration of zSignFedAvg balances privacy and convergence speed, accelerating convergence and improving robustness, particularly for class imbalance. Notably, SAFE-IDS is the first federated network intrusion detection system that effectively maintains privacy while adeptly managing non-IID data. Our empirical evaluation demonstrates that SAFE-IDS achieves an impressive accuracy of up to 99.74% across various IDS datasets and a varying number of clients, proving its effectiveness in both securing client data and maintaining high model performance.