Toward Sustainable Environmental Intelligence: A Comprehensive Survey of Federated Learning Applications and Technical Challenges

Environmental monitoring systems increasingly face the challenge of processing vast volumes of distributed, heterogeneous, and sensitive data in real time. Federated Learning (FL), a decentralized machine learning paradigm, offers a compelling solution by enabling collaborative model training across edge devices while preserving data privacy. This paper presents a comprehensive review of recent advancements in FL-driven environmental monitoring across domains such as air quality forecasting, traffic flow optimization, solar energy prediction, water quality assessment, carbon footprint analysis, and disaster response. We analyze how state-of-the-art FL methodologies, including diverse machine and deep learning models, hierarchical and multi-task architectures, semantic communication, and privacy-preserving aggregation, enhance prediction accuracy, communication efficiency, and robustness. However, FL adoption still faces critical challenges including communication overhead, non-IID data, sensor unreliability, and adversarial threats. To address these, we highlight key research directions such as energy-aware FL protocols, scalable and secure aggregation, real-world testbeds, and incentive mechanisms. By bridging insights from AI, wireless networking, and environmental science, this review lays the groundwork for establishing FL as a cornerstone of sustainable, privacy-aware, and intelligent environmental monitoring systems.