TL;DR
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 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.
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.
A unified online log anomaly detection framework, FLMLog, which is based on federated Learning and large language model, and adopts a prefix-aware in-context learning (ICL) refinement strategy, which achieves an improvement in prefix caching efficiency.
A lightweight FL-based IDS tailored for drone swarm networks using deep neural networks (DNN) enhanced with knowledge distillation (KD) to reduce model complexity and communication costs without sacrificing detection performance.
The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity risks by exposing distributed connected devices to increasingly complex and pervasive threats. Conventional centralized security mechanisms often struggle to accommodate the heterogeneous and decentralized structure of IoT networks. This study investigates Federated Learning (FL) as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead. A novel FL-based Intrusion Detection System (IDS) architecture was developed using Convolutional Neural Networks (CNNs) for anomaly detection and the Federated Averaging (FedAvg) algorithm for aggregating local model updates. The framework was evaluated on standard IoT datasets under non-independent and identically distributed (non-IID) data conditions to simulate heterogeneous real-world environments. Experimental results demonstrate that the proposed system achieved a detection accuracy of 94.6%, an F1-score of 93.8%, and a recall of 92.7%, outperforming centralized and standalone local learning methods. The framework also reduced communication overhead by 35% and achieved convergence 28% faster than conventional approaches. These findings demonstrate that FL can provide a scalable, privacy-preserving, and computationally efficient foundation for strengthening IoT cybersecurity. This study contributes a decentralized machine-learning architecture for real-time, adaptive, and privacy-conscious intrusion detection in large-scale IoT environments.
This review underscores the potential of FL to become a foundational technology in next-generation cybersecurity systems, enabling scalable and privacy-preserving threat mitigation across distributed infrastructures.
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