Energy-Aware Federated Learning for Climate Monitoring

Artificial Intelligence (AI) plays a vital role in climate monitoring, biodiversity assessment, and disaster prediction; however, traditional centralized AI models demand high computational power and energy, increasing carbon emissions. This chapter introduces an energy-aware Federated Learning (FL) framework that decentralizes model training across distributed sensors to minimize communication overhead, preserve data privacy, and reduce energy consumption. By employing lightweight models, adaptive aggregation, and renewable-powered infrastructure, the framework achieves accurate, low-carbon environmental intelligence. It positions FL as a sustainable “green AI” solution for scalable, privacy-conscious, and climate-resilient monitoring systems.