Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications
Ηλίας Δρίτσας, Μαρία Τρίγκα
Abstract
Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are paramount. This survey provides a comprehensive overview of FL, focusing on its integration with the IoT. We delve into the motivations behind adopting FL for IoT, the underlying techniques that facilitate this integration, the unique challenges posed by IoT environments, and the diverse range of applications where FL is making an impact. Finally, this submission also outlines future research directions and open issues, aiming to provide a detailed roadmap for advancing FL in IoT settings.