A Decentralized Learning Architecture for Medical Prescription Anomaly Detection via Hybrid Federated-Swarm Learning

A review of federated learning: architectures, challenges, and targeted solutions

Enhanced Intrusion Detection in IoT Networks using Federated Learning

Existing Federated intrusion detection system (IDS) solutions unfittingly deploy homogeneous models across all devices regardless of resource constraints, lack real-time detection capabilities due to synchronous federated rounds, and fail to balance the trade-off between lightweight edge processing and deep analytical accuracy. This paper addresses these through intelligent workload distribution between edge and cloud layers. The system uses a dual-detection mechanism where lightweight Decision Tree (DT) models on IoT devices provide immediate threat classification with confidence scoring. High-confidence predictions enable instant response to attacks without waiting for the server, while uncertain cases are sent to a CNN-LSTM model for deep analysis. In the federated learning component, edge devices share only model corrections from server feedback rather than continuous parameter updates. The central server aggregates these learning experiences to refine a global model and redistributes the updates back to edge devices. This selective knowledge sharing reduces communication overhead while enabling continuous improvement of local models and ensuring privacy. The performance of the system was evaluated using the IoT-23 dataset. Our approach achieved an accuracy of 98.08% on edge detection and 95.27% in the overall asynchronous setup. These results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments.

An Adaptive Federated Learning with XGBoost Ensembles for Intrusion Detection in Heterogeneous IoT Networks

: The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for decentralized intrusion detection in heterogeneous IoT environments. The framework dynamically adjusts both participating clients and local training workload at each communication round using a Linear Upper Confidence Bound (LinUCB) contextual bandit controller. The proposed Adaptive-FL model is based on XGBoost boosters and uses quality-weighted server-side ensemble aggregation. At the same time, adaptation is guided by a multi-objective reward that balances classification performance, training latency, communication overhead, and computational cost. The framework is evaluated on CIC IoMT 2024 and RT-IoT2022 under realistic non-IID conditions using stratified 5-fold cross-validation and benchmarked against Static-FL, FedAvg-FL, and a centralized XGBoost upper bound. Experimental results demonstrate that Adaptive-FL outperforms all federated baselines across both datasets, achieving Macro-F1 scores of 98.27% on RT-IoT2022 and 94.21% on CIC IoMT 2024, with statistically significant improvements over Static-FL on both datasets. Adaptive-FL maintains superior classification stability while avoiding raw-data centralization. It remains within 0.67 and 0.35 percentage points of the centralized upper bounds on RT-IoT2022 and CIC IoMT 2024, respectively.

Resilience in Federated Learning-Based IoMT Applications

Internet of Medical Things (IoMT) applications require collaborative learning across healthcare institutions while ensuring patient data privacy. Traditional centralized learning approaches require sharing sensitive medical records, increasing privacy and security risks. Federated Learning (FL) enables distributed model training by exchanging model parameters instead of raw data, but its performance is affected by client dropouts and communication failures. This paper proposes a Resilience-Enhanced Federated Learning Framework for IoMT applications that improves the reliability of collaborative learning under unstable network conditions. The framework incorporates Federated Averaging (FedAvg), resilient aggregation using historical model updates with staleness decay, and quantized model updates to reduce communication overhead. The proposed model was evaluated using a heart disease dataset distributed across multiple healthcare clients. Experimental results demonstrate that the framework achieves 95.72% accuracy while maintaining stable model convergence during client failures. The proposed approach provides a secure, privacypreserving, and fault-tolerant solution for distributed healthcare applications.

FedE: Protecting Training Data of Federated Learning Based on Multi-Precision Functional Encryption

: With the rapid development of artificial intelligence technologies in machine learning-as-a-service (MLaaS), deep learning-based intelligent models have demonstrated high value in applications such as trend prediction and risk assessment. However, MLaaS data are typically highly sensitive and contain private information. In cross-institutional collaborative modeling scenarios, different departments and local centers often hold partial, heterogeneous data resources on MLaaS platforms. Given data security, privacy, and compliance requirements, raw data are difficult to share directly, which makes it challenging to apply centralized model training methods. This paper proposes FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training method based on functional encryption. By introducing a multi-precision joint-computation mechanism, this method enhances numerical adaptation during ciphertext computation. Besides, combined with differential privacy techniques, it prevents model parameter updates from easily compromising privacy in cross-institutional federated learning. Based on the federated learning training process, a secure training framework for sensitive MLaaS data is constructed. Finally, experiments demonstrate the security and efficiency of the proposed approach.

Federated Anomaly Detection for IoMT Networks: Privacy‐Preserving Design, Lightweight Implementation, and Runtime Evaluation

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, heterogeneous devices, and sensitivity of medical data. In this paper, we propose a novel federated learning‐based anomaly detection system (Fed‐ADS) designed specifically for IoMT networks. Our system leverages local training of lightweight ML models on resource‐constrained IoMT devices and employs secure model aggregation at the gateway to preserve privacy and avoid centralized data collection. To address real‐world challenges, we implement and evaluate our system on a real IoMT testbed using Raspberry Pi devices under various attack scenarios. Furthermore, we examine the impact of privacy‐preserving techniques such as differential privacy on detection accuracy and system overhead. The runtime evaluation shows that our approach achieves high detection accuracy (over 94%) with minimal CPU and memory usage (under 3%), making it suitable for practical deployment in medical environments.