FedMamba-IoMT: Federated state space models with differential privacy and byzantine resilience for privacy-preserving intrusion detection in Internet of Medical Things
The proliferation of Internet of Medical Things (IoMT) devices has created critical cybersecurity challenges demanding intrusion detection systems that achieve high accuracy across diverse attack taxonomies while preserving patient privacy across institutional boundaries. Existing federated learning (FL) approaches face an inherent tension: Transformer-based architectures achieve strong detection performance but incur quadratic computational complexity and substantial communication overhead, while lightweight classifiers sacrifice representational capacity. Moreover, most FL-based intrusion detection systems lack formal privacy guarantees and robustness against adversarial participants. This paper introduces FedMamba-IoMT, the first federated State Space Model framework for privacy-preserving intrusion detection in IoMT networks, incorporating differential privacy (DP-SGD), Byzantine-resilient aggregation, and multi-level explainability. The proposed architecture reformulates tabular network traffic features as pseudo-sequential tokens processed through stacked selective State Space Model (Mamba) blocks with gated residual connections, achieving linear computational complexity 𝒪(n) with 78% fewer parameters than Transformer alternatives. We design a novel FedMamba aggregation strategy that weights client contributions by a convex combination of dataset proportion and inverse validation loss, augmented with a cosine similarity-based Byzantine filter that detects and excludes malicious model updates. Integration of DP-SGD with Rényi differential privacy accounting provides formal privacy guarantees (ε∈{1.0,2.0,3.0,5.0,8.0}, δ=10−5) while maintaining competitive accuracy. Comprehensive evaluation across three benchmark datasets—Edge-IIoTset (2,219,201 samples, 15 classes), CICIoMT2024 (3,204,537 samples, 19 classes), and Gotham Dataset 2025 (496,191 samples, 8 high-level traffic categories)—demonstrates that FedMamba-IoMT achieves 99.47±0.04%, 99.52±0.04%, and 98.90±0.04% multiclass accuracy without DP, and 98.52%, 98.18%, and 97.16% at ε=3.0, surpassing all prior federated IDS approaches. Byzantine resilience experiments demonstrate that the proposed defense maintains >95% accuracy under 30% malicious clients across label-flipping, model poisoning, and free-rider attacks. Gradient inversion analysis confirms that FedMamba’s compact parameterization (135K parameters, 0.52 MB) provides 2× higher reconstruction error compared to Transformer-based FL, and the integrated SHAP and LIME explainability framework supports regulatory compliance with the FDA’s 2023 cybersecurity guidance for medical devices.