<ul><li>Federated Learning (FL) offers a decentralized approach for air quality monitoring, enabling collaborative model training without sharing raw data.</li><li>FL applications in air quality and environmental monitoring are effective in predicting pollutants and managing environmental data.</li><li>Challenges of FL in this domain include communication overhead, infrastructure demands, generalizability issues, computational complexity, and security vulnerabilities.</li><li>Future research should focus on optimizing communication protocols and reducing the frequency of updates to address challenges and enhance the applicability of FL in real-world environmental monitoring scenarios.</li></ul>

Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances

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