Federated learning for sustainable IoT: Decentralized intelligence and energy-efficient applications

Federated learning (FL), as a decentralized machine learning paradigm, emerges as a pivotal approach to addressing the ecological challenges posed by traditional IoT systems. While existing research extensively explores FL in smart cities and healthcare, its potential for fostering sustainable IoT practices remains underexplored. This review fills this gap by exploring how FL can help in lowering the amount of carbon footprint and energy use of centralized IoT infrastructures. In comprehensive analyses, this study highlights the integration of FL with green computing concepts, it’s usage in various fields, including environmental monitoring and smart grids, and how it can interact with blockchain technology. Across selected case studies, federated learning is reported to improve runtime- or compute-related efficiency and predictive performance in specific environmental sensing settings, and FL–blockchain designs in smart-city settings are reported to reduce latency under the studied simulation assumptions. Despite these advancements, challenges like data heterogeneity, resource limitations, and privacy concerns exist. Proposed solutions include lightweight FL models, secure aggregation protocols, and adaptive resource allocation strategies. This review underscores FL’s transformative role in achieving a sustainable IoT ecosystem and identifies future research directions for robust and scalable green IoT implementations. • Federated Learning reduces IoT carbon footprint and energy consumption significantly. • Blockchain integration enhances data-sharing security and reduces latency by 20–30 ms. • FL improves computational efficiency up to 7.3 times and accuracy by over 13.2%. • Data heterogeneity and limited resources hinder the integration of FL in green IoT. • Lightweight FL models and secure protocols enhance efficiency and scalability.