Jul 2026· Annual International Computer Software and Applications Conference· pp. 2142-2147· 0 citations· 41 references
Abstract
Traffic congestion significantly impacts safety and urban livability in smart cities, motivating the development of accurate Traffic Flow Prediction (TFP) systems. Traditional deep learning approaches typically rely on centralized training, which is difficult to scale in distributed Internet of Things (IoT) environments. To address these limitations, decentralized paradigms such as Local Learning (LL) and Federated Learning (FL) enable on-device training and collaborative model updates while preserving data locality. For real-time TFP, the inherently non-stationary nature of traffic data necessitates continuous model adaptation, making online federated learning essential for scalable and collaborative deployment. However, this setting remains challenging because traffic data are typically non-IID across clients, with local patterns varying significantly across locations and devices. This paper presents an exploratory study of online federated learning for TFP on resource-constrained IoT devices. Using a GRU-based network as a common backbone, the Online Federated Learning paradigm is benchmarked relative to Centralized and Local Learning as reference baselines. A performance evaluation was conducted by evaluating RMSE and MAE on the PEMS-BAY dataset. Robustness to non-IID data is further assessed using FedProx and SCAFFOLD. Results show that LL achieves the lowest prediction error, whereas FL degrades as the number of local epochs increases, and non-IID mitigation strategies provide limited improvements under low-latency constraints. Overall, online federated learning is a viable approach for real-time TFP, but its performance is highly sensitive to client heterogeneity.
The Federated Green Anaconda Optimizer (FedGAO), an innovative FL framework inspired by the behavioral patterns of the Green Anaconda Optimizer (GAO), is proposed, demonstrating superior performance in terms of accuracy, convergence speed, and resource efficiency.
Elahe Eslami, S. A. Shahzadeh Fazeli, J. Abouei et al.· Cluster Computing· 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
Results confirm that reinforcement learning–based resource allocation provides a scalable and effective solution for IoT networks, particularly in environments characterized by large state spaces, dynamic network conditions, and stochastic traffic patterns.
L. Hoang, Van-Tam Hoang, Huu-Huy Ngo· International journal of Com...· 1 citation
FedTraffic, a hierarchical federated learning framework for traffic flow forecasting that integrates Edge–Fog–Cloud computing, hybrid deep learning, adaptive federated optimization, and Explainable Artificial Intelligence, is proposed.
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