Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-6· 0 citations· 10 references
Abstract
This work addresses the challenges of energy efficiency and Quality of Service (QoS) in dynamic Internet of Things (IoT) networks, where traditional routing protocols fail to adapt to changing conditions. To overcome these limitations, a Stochastically Enhanced Multi-Agent Federated Reinforcement Learning (SE-MA-FRL) framework is proposed. The method integrates multi-agent reinforcement learning with federated learning and stochastic policy exploration to enable distributed, privacy-preserving, and adaptive routing decisions. The model uses parameters such as residual energy, link quality, queue length, and distance to optimize routing. Simulation results demonstrate that the proposed approach achieves a Packet Delivery Ratio (PDR) of 96.96%, outperforming conventional and existing RL-based methods, while also reducing delay, packet loss, and energy consumption. In conclusion, SE-MA-FRL provides a scalable, reliable, and energy-efficient routing solution suitable for largescale and dynamic IoT environments with strict QoS requirements.
Simulation results validate the holistic integration with federated multi-agent learning, with emphasis on contention awareness and energy balancing is essential for scalable and efficient routing in next-generation dense FANETs.
H. Khujamatov, Elyanora Jolimbetova, Khaleel Ahmad et al.· Journal of Computer Science· 0 citations
A federated reinforcement learning framework for adaptive load balancing in the edge-fog-cloud continuum that optimizes energy efficiency and supports diverse quality of service requirements and uses a distributed experience replay buffer to reduce trial-and-error in reinforcement learning.
Si Liu, Midhun Chakkaravarthy· Future Technology· 0 citations
This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.
Mehdi Hosseinzadeh, Parisa Khoshvaght, Amir Masoud Rahmani et al.· Cluster Computing· 0 citations
Experimental results show that the proposed OPHM-DQN-ADBOA framework consistently reduces EC, execution delay, and SLA violations while improving resource utilisation and LB and the model achieves high scalability and adaptability in heterogeneous IoT-Cloud resource management.
A. Ali, A. R. Mohamed Shanavas· THE SCIENTIFIC TEMPER· 0 citations
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
This study proposes an intelligent Q-learning-enhanced Evolutionary Game Theory (QEGT) routing mechanism for USNs that leverages game-theoretic incentives and Q-learning to adaptively select strategies.
Anita Murmu, Saurabh Kumar Srivastava, Nuthan Chingeetham et al.· IEEE Open Journal of the Com...· 0 citations
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