Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 1632-1637· 0 citations· 22 references
Abstract
Federated Learning (FL) for Internet of Things (IoT) and cyber-physical sensor networks, such as aquaculture water monitoring, faces critical challenges due to temporal sensor drift, non-IID data distributions, and communication constraints across edge devices, which compromise global model stability and resource efficiency. However, most existing federated approaches lack coherent mechanisms to address these issues, leading to degraded performance in realistic edge deployments. We present AutoML-DAFL, a drift-aware, AutoML-guided federated learning framework with a multi-objective reward controller that jointly optimizes predictive accuracy, model consistency, and communication efficiency. The framework integrates temporal drift detection and mitigation into the federated training loop through MAE-based regularization while ensuring persistent convergence. To evaluate its effectiveness, we benchmark AutoML-DAFL against FedAvg and advanced baselines, including FedNova and SCAFFOLD. Extensive ablation studies and comparative analysis on real aquaculture monitoring data demonstrate the contribution of each reward component: removing the communication-aware term degrades model consistency, while excluding MAE-based smoothing reduces training stability. The full AutoML-DAFL configuration, integrating all reward components through multi-objective optimization, achieves the lowest RMSE (0.0721), highest R2, and improved fairness across clients, demonstrating strong resilience to non-IID drift and bandwidth constraints. These results highlight the effectiveness of drift-aware AutoML optimization for resource-efficient, stable federated forecasting in cyber-physical monitoring systems.
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
There are significant gaps that remain in terms of model interpretability and the ability to generalize across climate variations, so this article provides a relatively comprehensive overview of the application of federated learning in air quality forecasting and monitoring.
Yuhao Wu· Mathematical Modeling and Al...· 0 citations
Solar-powered sensor networks are reshaping agriculture by enabling continuous farm management and animal welfare monitoring through Internet-of-Things (IoT) devices, edge intelligence, and cloud analytics. Yet, their sustainability is threatened by two underexplored challenges: vulnerability to cyber-attacks and instability under fluctuating energy supplies. To address these issues, we propose a sustainable smart farm framework that ensures reliable monitoring under adversarial and resource-constrained conditions. Our approach employs deep reinforcement learning (DRL) to derive adaptive policies that jointly optimize monitoring fidelity and energy efficiency. To overcome DRL’s slow convergence and unstable adaptation in dynamic environments, we design a decision theory (DT)-guided transfer learning (TL) framework. Embedding DT principles accelerates policy learning while balancing trade-offs among monitoring quality, energy sustainability, and system resilience. Experimental evaluations on realistic farm scenarios show that DT-guided DRL reduces training runtime by 47.5% while outperforming TL-enhanced DRL in robustness and monitoring performance. This work makes three contributions: (1) unifying cyber resilience and energy adaptability in agricultural monitoring, (2) advancing methodological innovation with DT-guided TL for stable DRL convergence, and (3) providing design insights for sustainable agricultural cyber-physical systems. Collectively, these contributions extend sensor networks in agriculture by emphasizing resilience, sustainability, and intelligent adaptation.
Dian Chen, Zelin Wan, D. Ha et al.· ACM Transactions on Cyber-Ph...· 0 citations
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.
M. Pizzolante, A. Shumba, T. Montanaro et al.· Annual International Compute...· 0 citations
Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%.
Shuo He, He-Yang Wei, Congxian Bi et al.· Electronics· 0 citations
It is demonstrated that lightweight aggregation strategies can substantially improve empirical safety in federated reinforcement learning while preserving standard communication protocols.
