Federated Learning–Based Green AI for Sustainable Energy Optimization in 6G WSN

Abstract

The advent of 6G wireless sensor networks (WSNs) demands highly efficient, sustainable, and intelligent energy management strategies to support massive device connectivity and data-driven applications. Traditional centralized learning models are energy-intensive and pose serious privacy risks, making them unsuitable for resource-constrained WSN nodes. In this work, we propose a Federated Reinforcement Learning–based Green AI framework (Fed-RL-GAI) for sustainable energy optimization in 6G WSNs. Our approach combines federated averaging (FedAvg) with an energy-aware deep reinforcement learning (DRL) agent at each node to minimize local energy consumption while optimizing global system performance. Specifically, nodes locally train lightweight DRL models to manage their sleep–wake cycles and transmission schedules based on dynamic environmental conditions, and periodically aggregate model updates without sharing raw data. We introduce an adaptive client selection mechanism that prioritizes nodes with higher residual energy and better local model quality to participate in each training round, further enhancing sustainability. Extensive simulations under realistic 6G WSN scenarios demonstrate that Fed-RL-GAI reduces energy consumption by up to 35%, extends network lifetime by 42%, and achieves faster convergence compared to traditional FL and centralized DRL baselines. This study establishes a viable pathway toward integrating federated learning and green AI principles for the sustainable, intelligent operation of future 6G WSNs.