RPPFL: Random Projection-Based Personalized Federated Learning for IoT Intrusion Detection

HomeGuard: Community-Driven Hierarchical Federated Learning for Robust Smart-Home Intrusion Detection

: Federated Learning (FL) has emerged as a promising approach to build collaborative Intrusion Detection Systems (IDSs) in the IoT, e.g., in smart homes. FL allows models to be shared without exposing sensitive training data, thus protecting the privacy of IoT users. However, existing FL-based IDSs rely on assumptions that rarely hold in practice, namely homogeneous devices, synchronous participation, and benign contributors. We argue that, in real-world smart homes, IoT devices are highly heterogeneous, resource-constrained, and attractive targets for adversaries, which makes conventional FL less effective or vulnerable to poisoning attacks. We present H OME G UARD , a collaborative IDS specifically designed for the constraints and threat model of practical smart home IoT infrastructures. In our approach, we rethink FL deployment by (1) of-floading model training to gateways to manage computational heterogeneity of IoT devices and (2) organizing anomaly detection models into device-specific communities based on privacy-preserving traffic fingerprints which do not expose sensitive data. Within communities and across smart homes, H OME G UARD implements an asynchronous, hierarchical FL architecture that tolerates device churn, uneven data availability, and Byzantine participants. Further, H OME G UARD applies Byzantine-robust aggregation at two levels: within local communities, and globally in the cloud to limit the impact of compromised devices. Experimental evaluation shows that H OME G UARD achieves an average true positive rate of 97.86% locally and 97.53% globally with a 0% false positive rate, while maintaining robustness against both targeted and untargeted poisoning attacks.

Federated Predictive Learning with Privacy-Aware Model Aggregation for Distributed Analytics

This study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework, suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent systems.

Privacy-preserving lightweight intrusion detection system for agricultural IoT based on federated learning and temporal convolutional network

Privacy-Preserving and Poisoning-Robust Federated Learning for Industrial IoT

With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency.

Federated Learning for Privacy-Preserving Anomaly Detection in Heterogeneous IoT Networks

Simulation of a Federated Learning framework for privacy-preserving anomaly detection tailored to heterogeneous IoT networks characterised by non-independent and identically distributed data, variable computational capacities, and intermittent connectivity indicates that the proposed method offers a practical, scalable, and regulation-compliant pathway toward trustworthy intrusion and anomaly detection in large-scale, heterogeneous IoT deployments.

Enhancing Scalability and Communication Efficiency in Sub-Network Based Federated Deep Learning for Multiclass Intrusion Detection in IoT Networks

This work proposes a novel multiclass intrusion detection system using FL at the subnetwork level using various machine learning models, including Artificial Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory to build an effective IoT IDS.