FL‐DQN‐DP‐LEACH: A Federated Reinforcement Learning Framework for Privacy‐Preserving and Energy‐Efficient Clustering in Wireless Sensor Networks

This work investigates the trade‐offs among energy efficiency, privacy, and adaptability in wireless sensor networks (WSNs), bridging the gap between legacy clustering protocols such as LEACH and the requirements of modern IoT deployments. While LEACH offers simplicity, its static, probabilistic approach is limited in dynamic and privacy‐sensitive environments. To address these challenges, we propose DQN‐FL‐DP‐LEACH, a coupled framework that integrates decentralized deep Q‐networks (DQNs) for energy‐aware clustering, federated learning (FL) for decentralized policy refinement, and an adaptive differential privacy (DP) mechanism for privacy‐protected aggregation under a trusted base station. Extensive simulations show that DQN‐FL‐DP‐LEACH increases network lifetime (last node death) by 1.7%–28.8% and first node death by up to 13.6% compared to LEACH across a wide range of scenarios. Under heterogeneous energy conditions, the protocol reduces performance degradation by 58.6%, experiencing only a 6.3% reduction in network lifetime versus LEACH′s 15.2% decline. The framework achieves up to 9.4% higher packet delivery ratio and maintains fairness, with Gini coefficients below 0.1 for more than 90% of rounds. The privacy results are reported as an adaptive round‐wise privacy‐control schedule together with final cumulative accounting bounds, avoiding the ambiguity between instantaneous privacy control and cumulative privacy loss. These findings demonstrate that DQN‐FL‐DP‐LEACH combines privacy‐aware aggregation, adaptive clustering, and improved communication reliability in diverse WSN conditions. The results highlight that context‐aware machine learning protocols with explicit privacy‐control mechanisms can provide measurable advantages for future IoT networks while still exposing practical limitations related to update compression assumptions and local training overhead.