Aug 2026· 2026 6th International Conference on Emerging Smart Technologies and Applications (eSmarTA)· pp. 1-6· 0 citations· 21 references
Abstract
Federated learning (FL) reduces raw-data sharing in Internet of Things (IoT) intrusion detection systems (IDSs), but standard FL can still overuse battery-powered clients by assigning local training without considering device health. This paper presents ResFed-IDS, a resource-aware FL framework that combines server-side healthy-client selection with client-side self-preservation. Clients are eligible only when battery is at least 30% and central processing unit (CPU) load is at most 0.85; the server then selects up to ⌈0.6K⌉ healthy clients per round and aggregates successful updates through sample-weighted federated averaging (FedAvg). On a balanced CICIoT2023 subset, ResFed-IDS reached 82.11% best accuracy in a 15-round, 5-client simulation with zero device depletion, whereas standard FedAvg reached 81.56% and produced 27 client-unavailability events with four ultimately depleted clients. The same policy transferred to CICIIoT2025 and CIC-ToN-IoT, yielding 78.87% and 69.35% best accuracy while preserving all devices. Relative to the centralized SimpleMLP baseline, the remaining gap is only 0.73 percentage points, indicating that most loss is architectural rather than caused by the resource-aware FL procedure.
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.
Chidera Winifred John, Eduediuyai Ekerete Dan, P. Asuquo et al.· E3S Web of Conferences· 0 citations
Internet of Things(IoT)/edge intrusion-detection systems operate on distributed traffic and system-state data whose distributions vary across gateways, services, and attack conditions. We study a server-assisted federated setting in which a teacher reference is fitted from a permitted server-accessible training pool and explicitly distinguish this simulation assumption from fully decentralized deployment. The proposed framework evaluates progressive local training through boundary stabilization, confidence-weighted decision distillation, representation alignment, and validation-quality-aware aggregation. The evaluation uses a leakage-controlled protocol: server and client validation subsets are held out before federated training, update quality and early stopping use validation data only, and the final-test split is evaluated once. Results on NSL-KDD, CIC-IDS2017, Edge-IIoTset, and the ToN-IoT network dataset show competitive primary performance and stronger robustness in several severe label-skew settings. On the Telemetry of Things(ToN-IoT) with Dirichlet alpha = 0.1, the proposed method achieves 91.46 ± 5.54 F1, compared with 53.73 ± 49.00 for FedAvg and 53.77 ± 48.92 for FedProx. The results do not establish universal superiority or a universally optimal stage order: competing methods remain stronger in selected stable and attack-shift settings. The framework is therefore presented as a bounded, server-assisted robustness-oriented training strategy for heterogeneous IoT/edge intrusion detection.
Xudong Yang, Zikui Lin, Qiuyan Li et al.· Electronics· 0 citations
This study demonstrates the efficacy of the synergy between federated learning and edge computing in IoT security contexts, providing a scalable and privacy-centric solution for anomaly detection across large-scale distributed devices.
Quan Liu, Yuanyuan Feng· Discover Artificial Intellig...· 0 citations
AF-BKM is presented, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, and identifies merge-induced precision decay under non-IID workers as an open gap.
Mohammed I. Al-Saleh, Joseph Azar· IoT· 0 citations
Federated Bandit Intrusion Detection (FBID), a novel adaptive PFL framework to address this limitation through server-side personalization control, employs a contextual multi-armed bandit at the server to dynamically regulate each client's local training intensity according to its observed behavior and update quality.
A. Bui, Cong T. Nguyen, Hoang-Anh Pham et al.· 0 citations
The proliferation of Internet of Things (IoT) devices operating at the network edge has created unprecedented demand for distributed machine learning capable of functioning under severe resource constraints. Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training across distributed nodes; however, its application to energy-constrained edge environments remains insufficiently characterized at the system level, particularly with respect to reproducible evaluation of resource consumption and communication efficiency. In this paper, we present EcoFL (Energy-Conscious Federated Learning), a modular, energy-aware benchmarking and orchestration framework for systematic evaluation of lightweight machine learning models under emulated edge hardware constraints. Rather than proposing a new federated optimization algorithm, EcoFL extends a standard FedAvg-based training pipeline with three principal components: (i) an energy-aware communication scheduler that dynamically adapts aggregation rounds and client participation based on per-node resource availability; (ii) a comprehensive system-level profiling pipeline capturing CPU utilization, RAM consumption, inference latency, communication overhead, and estimated computational energy consumption per training round; and (iii) a reproducible benchmarking methodology enabling fair comparison of centralized, standard federated (FedAvg), and energy-aware federated configurations. We evaluate five lightweight model families—Logistic Regression, Random Forest, XGBoost, Multilayer Perceptron, and Isolation Forest—under emulated Raspberry Pi 4 hardware constraints using an anomaly detection task on synthetic IoT sensor telemetry (50,000 samples, 12 features, Dirichlet non-IID partitioning). Experimental results across five independent seeds show that, within the evaluated benchmark setting, EcoFL reduces estimated federated training energy by 79.9–92.9% (mean 84.4%) relative to standard FedAvg through adaptive round termination (4–7 rounds versus 20 fixed rounds), while showing no statistically significant F1-score degradation for four of the five evaluated model families under the tested seed regime. Notably, EcoFL achieves a higher F1-score than FedAvg for Random Forest (+0.052), which we attribute to reduced overfitting resulting from earlier convergence under non-IID data distributions. The full EcoFL framework is released as open-source software to promote reproducibility in energy-aware federated learning research and to facilitate systematic investigation of the trade-offs between predictive performance, resource utilization, and communication overhead in resource-constrained edge environments.
Tymoteusz Miller, Irmina Durlik· Journal of Low Power Electro...· 0 citations
