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.
Abstract
Energy efficiency remains a major challenge in deploying IoT systems, especially in scenarios requiring large numbers of devices while balancing computational requirements and operational lifetimes. This paper proposes 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. The proposed framework addresses the limitations of traditional centralized machine learning approaches that require collecting sensitive operational information and transmitting it to cloud servers for centralized analysis. This increases the risk of privacy violations and introduces communication overheads that limit the responsiveness of IoT systems. The proposed framework employs a federated reinforcement learning approach, enabling edge nodes to collaboratively learn an optimal load-balancing policy without transmitting operational information. The proposed framework uses a context-aware reward function that optimizes multiple objectives based on temporal patterns, device energy levels, and application criticality. This enables the proposed framework to adapt its optimization objectives and balance energy efficiency and performance maximization. The proposed framework introduces a new action-space pruning mechanism that accelerates the optimization process by leveraging domain knowledge of possible load-balancing patterns. The proposed framework uses a distributed experience replay buffer to reduce trial-and-error in reinforcement learning. The proposed framework demonstrates its effectiveness in optimizing energy efficiency through a series of experiments in a real-world IoT environment and a centralized machine learning approach. The proposed framework demonstrates that distributed machine learning approaches can outperform centralized ones for optimizing energy efficiency in IoT systems.
The proposed framework is validated by conducting simulation-based experiments on the benchmark datasets and synthetic autonomous workloads, where the novelty lies in the design of the system-level federated learning architecture, instead of the datasets themselves.
Jyotsnarani Tripathy, D. Rajalakshmi, A. N. Ramya Shree et al.· SN Computer Science· 0 citations
This work exploits the concept of cooperative communication and radio frequency-based energy-harvesting to improve the network throughput while maintaining power supply to the IoTDs and employs the reinforcement learning frameworks, particularly state–action–reward–state–action (SARSA) and Q-learning.
Olumide Alamu, T. Olwal, Emmanuel M. Migabo· Network· 0 citations
The distribution of renewable energy resources and Edge-IoT infrastructures have brought new challenges in intelligent smart microgrid management, such as dynamic-energy-demand changes, carbon-heavy energy-scheduling, communication overhead, and battery degradation. The deployment of renewable energy resources, distributed battery storage systems, and Edge-IoT infrastructures has created a number of challenges in the management of smart microgrids, such as dynamic changes in energy demand, carbon-heavy energy-scheduling, communication overhead, and battery degradation. To tackle these challenges, this paper introduces a personalized Federated Deep Reinforcement Learning (GridMind-FDRL) framework for carbon-aware and battery-safe decentralized smart microgrid optimization. The proposed framework combines federated learning, deep reinforcement learning, edge intelligence, carbon-aware energy scheduling and battery-aware adaptive optimization with a centralized framework for energy management. Unlike traditional centralized optimization methods, GridMind-FDRL allows for collaborative learning among distributed microgrid nodes while maintaining privacy and enabling low latency real-time optimization in dynamic Edge-IoT environments. The framework was tested with different operating conditions of intermittent renewables, varying load levels, and battery stress conditions. The results of the experiment showed that the energy efficiency could be 93.86%, carbon reduction 24.36%, battery health preservation 90.42%, communication efficiency 88.08%, and decision latency reduction 34.21%. The acquired results support the scalability, sustainability, and smart energy optimization ability of the suggested framework for the next-generation decentralized smart energy ecosystems.
Jaichandran R, P. Marimuthu, K.Nethra Devi et al.· 2026 6th International Confe...· 0 citations
Federated Reinforcement Learning (FRL) provides a useful basis for distributed policy learning in Edge-IoT systems, where clients interact with local environments without transferring raw operational data to a central server. Yet aggregation becomes difficult when clients operate under different transition dynamics, workloads, resource capacities, communication conditions, and operational constraints. In these settings, local policy updates may not differ only in magnitude or direction; they may also differ in stability, reliability, resource support, and operational feasibility. Conventional averaging is therefore limited, since it does not distinguish stable and feasible updates from unstable or constraint-violating ones. This paper examines aggregation stability and feasibility-sensitive aggregation in FRL for Edge-IoT systems. It reviews and synthesizes related literature across four connected streams: heterogeneous Federated Learning, FRL-based edge decision-making, constrained and safe Reinforcement Learning, and adaptive or reliability-aware aggregation. The reviewed studies are analyzed through six dimensions: learning paradigm, type of heterogeneity, role of policy learning, treatment of operational constraints, aggregation strategy, and whether local feasibility signals influence global aggregation weights. The analysis indicates that existing studies provide valuable foundations, but they usually treat heterogeneity, constraint handling, and aggregation adaptation as separate concerns. The paper identifies a need for aggregation mechanisms that jointly account for update stability, update reliability, resource availability, and constraint feasibility. It positions aggregation as adaptive client influence regulation rather than passive averaging in future Edge-IoT FRL systems.
Majid A. Aslan, A. Al-Shalabi, Ahmed S. Alhegami· مجلة جامعة صنعاء للعلوم التط...· 0 citations
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, is presented.
Tymoteusz Miller, Irmina Durlik· Journal of Low Power Electro...· 0 citations
A dynamic reward structuring framework within deep reinforcement learning to enable adaptive and balanced routing in IoT-WSNs and achieves significant performance gains, including approximately 30% improvement in energy efficiency, 25% reduction in latency, and 35% increase in network throughput compared with baseline methods.
Suresh Betam, S. Nagendram, Bathula Prasanna Kumar et al.· Scientific Reports· 0 citations
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