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Medical healthcare has experienced a revolution through the Internet of Medical Things (IoMT) system, which needs crucial improvements for security measures while working towards better privacy and efficiency capabilities. This paper develops asynchronous reinforcement federated learning (FL) with task offloading (ARFL‐TO) as a combination of FL with reinforcement learning (RL) through dynamic TO systems to boost scalability alongside adaptability and security in heterogeneous environments, including autonomous systems, smart grids, and industrial Internet of Things (IIoT) settings. ARFL‐TO requires validation through different datasets, which encompass a range of disease groups together with multiple medical instruments. The analysis indicates that ARFL‐TO surpasses FL‐TO and federated reinforcement‐based fusion (FRF) in productivity by 42.22% and reduces power requirements by 79.22% while shortening processing time by 7.13% during real‐world operation, where networks become unstable and data transmission is affected. This framework achieves privacy protection through secure data pooling, and it makes models understandable for clinical support in addition to delivering enhanced energy efficiency for low‐power medical devices. Future investigation directs toward integrating the proposed system into real healthcare environments and developing performance improvements for severe operational scenarios, and introducing adaptable hyperparameter techniques suitable for healthcare systems requiring dynamic adjustments. ARFL‐TO establishes itself as an efficient framework for secure time‐sensitive decision‐making across decentralized medical networks and cross‐sectoral systems, maintaining privacy concerns.

Using Google cluster traces, the research presents a task offloading algorithm and a hybrid forecasting model that unites Bidirectional Long Short-Term Memory (BiLSTM) with Gated Recurrent Unit (GRU) layers along an attention mechanism. This model predicts resource usage for flexible task scheduling in Internet of Things (IoT) applications based on edge computing. The suggested algorithm improves task distribution to boost performance and reduce energy consumption. The system’s design includes collecting data, fusing and preparing it for use, training models, and performing simulations with EdgeSimPy. Experimental outcomes show that the method we suggest is better than those used in best-fit, first-fit, and worst-fit basic algorithms. It maintains power stability usage among edge servers while surpassing old-fashioned heuristic techniques. Moreover, we also propose the Deep Deterministic Policy Gradient (D4PG) based on a Federated Learning algorithm for adjusting the participation of dynamic user equipment (UE) according to resource availability and data distribution. This algorithm is compared to DQN, DDQN, Dueling DQN, and Dueling DDQN models using Non-IID EMNIST, IID EMNIST datasets, and with the Crop Prediction dataset. Results indicate that the proposed D4PG method achieves superior performance, with an accuracy of 92.86% on the Crop Prediction dataset, outperforming alternative models. On the Non-IID EMNIST dataset, the proposed approach achieves an F1-score of 0.9192, demonstrating better efficiency and fairness in model updates while preserving privacy. Similarly, on the IID EMNIST dataset, the proposed D4PG model attains an F1-score of 0.82 and an accuracy of 82%, surpassing other Reinforcement Learning-based approaches. Additionally, for edge server power consumption, the hybrid offloading algorithm reduces fluctuations compared to existing methods, ensuring more stable energy usage across edge nodes. This corroborates that the proposed method can preserve privacy by handling issues related to fairness in model updates and improving efficiency better than state-of-the-art alternatives.

Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type but with slightly different dynamics. For such multiple IoT devices, there is no guarantee that an agent who interacts only with one IoT device and learns the optimal control policy will also control another IoT device well. Therefore, we may need to apply independent reinforcement learning to each IoT device individually, which requires a costly or time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent IoT device shares their learning experience (i.e., the gradient of loss function) with each other, and transfers a mature policy model parameters into other agents. They accelerate its learning process by using mature parameters. We incorporate the actor–critic proximal policy optimization (Actor–Critic PPO) algorithm into each agent in the proposed collaborative architecture and propose an efficient procedure for the gradient sharing and the model transfer. Using multiple rotary inverted pendulum devices interconnected via a network switch, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices and that the learning speed can be faster if more agents are involved.

The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have been several successful applications of IoT in the industry. For example, in transportation systems, the novel internet of vehicles (IoV) concept has enabled new research directions and automation solutions. Moreover, reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated remarkable success in solving complex problems in different applications. In recent years, new solutions have been developed based on this combined framework (i.e., federated reinforcement learning). Conversely, there is a lack of analysis concerning IoT applications and a standard view of challenges and future directions of the current FRL landscape. Thereupon, the main goal of this research is to present a literature review of federated reinforcement learning (FRL) applications in IoT from multiple perspectives. We focus on analyzing applications in multiple areas (e.g., security, sustainability and efficiency, vehicular solutions, and industrial services) to highlight existing solutions, their characteristics, and research gaps. Additionally, we identify key short- and long-term challenges leading to new opportunities in the field. This research intends to picture the current FRL ecosystem in IoT to foster the development of new solutions based on existing challenges.

This paper presents an innovative intelligent decision optimization model that integrates distributed blockchain technology with federated reinforcement learning to address critical challenges in ship traffic collaborative supervision. Traditional maritime traffic monitoring systems suffer from data silos, privacy concerns, and centralized decision-making bottlenecks that impede effective multi-jurisdictional coordination. The proposed framework employs a multi-layered architecture consisting of data layer, blockchain layer, federated learning layer, and decision layer to enable secure data sharing while preserving operational autonomy among maritime authorities. The distributed blockchain mechanism ensures data integrity and immutability through cryptographic protocols and smart contracts, while the federated reinforcement learning algorithm enables privacy-preserving collaborative model training without exposing sensitive commercial information. Experimental validation demonstrates superior performance with 93.6% decision accuracy, 520ms average response time, and 285 transactions per second throughput. Case studies involving emergency collision avoidance, abnormal behavior identification, and search-and-rescue coordination confirm the system’s practical effectiveness, achieving 40% reduction in incident response times and 60% enhancement in cross-agency collaboration efficiency. The research provides a robust foundation for next-generation maritime traffic management systems that require secure multi-party collaboration and intelligent decision optimization.

This paper proposes a privacy-preserving energy management of a shared energy storage system (SESS) for multiple smart buildings using federated reinforcement learning (FRL). To preserve the privacy of energy scheduling of buildings connected to the SESS, we present a distributed deep reinforcement learning (DRL) framework using the FRL method, which consists of a global server (GS) and local building energy management systems (LBEMSs). In the framework, the LBEMS DRL agents share only a randomly selected part of their trained neural network for energy consumption models with the GS without consumer’s energy consumption data. Using the shared models, the GS executes two processes: (i) construction and broadcast of a global model of energy consumption to the LBEMS agents for retraining their local models and (ii) training of the SESS DRL agent’s energy charging and discharging from and to the utility and buildings. Simulation studies are conducted using one SESS and three smart buildings with solar photovoltaic systems. The results demonstrate that the proposed approach can schedule the charging and discharging of the SESS and an optimal energy consumption of heating, ventilation, and air conditioning systems in smart buildings under heterogeneous building environments while preserving the privacy of buildings’ energy consumption.

This paper proposes a novel decentralized federated reinforcement learning (DFRL) framework that integrates deep reinforcement learning (DRL) with decentralized federated learning (DFL). The DFRL framework boosts efficient virtual instance scaling in Mobile Edge Computing (MEC) environments for 5G core network automation. It enables multiple MECs to collaboratively optimize resource allocation without centralized data sharing. In this framework, DRL agents in each MEC make local scaling decisions and exchange model parameters with other MECs, rather than sharing raw data. To enhance robustness against malicious server attacks, we employ a committee mechanism that monitors the DFL process and ensures reliable aggregation of local gradients. Extensive simulations were conducted to evaluate the proposed framework, demonstrating its ability to maintain cost-effective resource usage while significantly reducing blocking rates across diverse traffic conditions. Furthermore, the framework demonstrated strong resilience against adversarial MEC nodes, ensuring reliable operation and efficient resource management. These results validate the framework’s effectiveness in adaptive and efficient resource management, particularly in dynamic and varied network scenarios.

The sudden changes in light and slope at the entrance of mountain tunnels can easily lead to unstable traffic flow, especially in mixed traffic flow containing ICV. The existing control methods are difficult to simultaneously address three challenges: dynamically changing lighting and slope environments, vehicle data privacy protection requirements, and insufficient adaptability to different tunnel scenarios. This study developed the MF3DQN-TF control framework to address these issues by integrating environmental perception and vehicle control. The framework first establishes a correlation model between light gradient and slope resistance, converting environmental risks into quantifiable control signals; Secondly, design an intelligent weight allocation mechanism to automatically increase the decision-making priority of environmental factors in strong light areas; Finally, a distributed training architecture is adopted to achieve knowledge sharing in multi tunnel scenarios while protecting vehicle data privacy. The verification results show that in typical tunnel testing scenarios, this framework significantly improves performance compared to traditional methods: the amplitude of speed fluctuations is reduced by about 40% compared to conventional control methods, the risk of rear end collisions is reduced to one-third of that of traditional reinforcement learning schemes, and the communication transmission volume is only half of that of typical federated learning methods. The verification results show that in typical tunnel testing scenarios, this framework reduces speed standard deviation by 41.4% (vs. PID control), cuts high-risk TTC < 2 s events by 62.5% (vs. centralized DQN), and nearly halves communication volume (46.2% reduction vs. FedProx). These improvements stem from the collaborative processing mechanism of the framework for environmental risks and vehicle status, providing a new technological path for safety control in complex tunnel environments.

The rapid proliferation of electric vehicles (EVs) and their spatially clustered charging behaviors have imposed unprecedented challenges on the stability, efficiency, and fairness of power distribution networks. Coordinating large-scale EV clusters across geographically distributed charging stations requires intelligent scheduling strategies that can simultaneously respect grid constraints, maximize user satisfaction, and enhance renewable energy utilization—all while safeguarding data privacy and computational scalability. This paper proposes a novel multi-agent cooperative dispatch framework based on Federated Deep Reinforcement Learning (FDRL) to optimize the real-time coordination between EVs, chargers, and the underlying power grid infrastructure. The model adopts a hierarchical structure where local agents independently train deep reinforcement learning policies tailored to site-specific dynamics, while a central aggregator synchronizes global model parameters using federated averaging enhanced by entropy-based reward normalization and fairness-aware weighting. The optimization problem is formulated as a multi-objective constrained Markov decision process (CMDP), featuring long-horizon coupling, grid-aware feasibility, and user-centric reward shaping. Our formulation explicitly integrates peak transformer loading limits, charging demand satisfaction, temporal renewable absorption, and inter-agent equity, thereby capturing the full complexity of EV–grid interactions. A realistic case study involving 1,200 EVs, 60 chargers, and a 33-bus feeder system over 24 hours shows that the proposed FDRL framework achieves a 13.6% reduction in grid operating cost, a 21.4% increase in renewable absorption, and fairness with Jain’s index consistently above 0.95, while reducing average state-of-charge (SoC) deviation to below 2.5% . These quantitative results highlight the effectiveness of the framework and confirm its promise as a privacy-preserving, scalable, and equitable solution for next-generation energy–cyber–physical systems.