TL;DR
Experimental results demonstrate that the federated LLM-based models consistently outperform a multilayer perceptron baseline, with the LLaMA model achieving up to 99.9% accuracy and F1-score while generalising effectively to previously unseen device types.
The results demonstrate that lightweight distribution-aware aggregation offers an effective, robust, and practically deployable solution for mitigating aggregation bias under simultaneous non-IID heterogeneity and severe multi-class imbalance in FL-based IoT botnet detection.
A more efficient IoT malware detection model based on an improved Federated Learning method that achieves good accuracy while strongly leveraging the advantages of Federated Learning in ensuring data privacy and minimizing computational resource usage during model training.
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
FedSE-1DSqueezeNet is proposed, a lightweight federated IDS tailored for resource-constrained IoT environments, designed to optimize feature extraction efficiency under strict resource constraints and achieves detection accuracy exceeding that of state-of-the-art models.
The rapid integration of Internet of Things (IoT) in the healthcare domain has led to the emergence of the Internet of Medical Things (IoMT), which introduces significant benefits in patient monitoring and real‐time medical services. However, IoMT networks are inherently vulnerable due to resource constraints, heteroge...
Mal-Fedchain is presented, a secure and privacy-preserving framework for image-based IoT malware detection and prevention that couples federated learning with blockchain and honeypot-assisted behavioral monitoring, targeting Linux-capable IoT gateway devices.
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