Federated and Reinforcement Learning Integration for Distributed Energy Optimization in Wireless Sensor Networks
Received: 21 September 2025 | Revised: 12 October 2025 and 26 October 2025 | Accepted: 27 October 2025 | Online: 29 November 2025
Corresponding author: R. Nivyashree
Abstract
Wireless Sensor Networks (WSNs) are very important for modern uses such as smart cities, environmental monitoring, and industrial automation. However, one of their biggest problems is still energy efficiency, since sensor nodes usually have limited battery life and communication needs that use a lot of energy. Conventional machine learning methods focused on improving energy efficiency often depend on centralized architectures, which are plagued by significant communication overhead, singular points of failure, and privacy vulnerabilities. This study introduces a Federated Reinforcement Learning (FRL) framework that combines Federated Learning (FL) with Deep Q-Networks (DQN) to enable distributed, adaptive, and privacy-preserving energy management in WSNs. The proposed system allows sensor nodes to train local models without sharing raw data, allowing them to learn the best energy decisions, such as which Cluster Head (CH) to choose, based on how the network changes in real time. The proposed FRL framework reduces overall energy use by up to 25%, extends the life of the network by 30%, and improves privacy preservation by 40%, all while keeping the accuracy of CH selection high (89.3%) and being strong even when 15% of nodes fail. These results show that FRL is much better than traditional LEACH and centralized DQN models when it comes to energy efficiency, scalability, and privacy. This study underscores the potential of integrating federated and reinforcement learning as a basis for next-generation intelligent, self-organizing, and energy-efficient WSNs.
Keywords:
Wireless Sensor Networks (WSNs), Federated Learning (FL), Reinforcement Learning (RL), Deep Q-Networks (DQN), energy efficiency, cluster head selection, privacy-preserving machine learning, decentralized learning, adaptive routing, edge intelligenceDownloads
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