Federated Learning for Internet of Things: A Comprehensive Survey

The Internet of Things (IoT) is penetrating many facets our daily life with the proliferation intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection processing that may not be feasible in realistic application scenarios due to high scalability modern IoT networks growing privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative approach can enable applications, allowing for training at devices without need sharing. In this article, we provide comprehensive survey emerging FL networks, beginning from an introduction recent advances discussion their integration. Particularly, explore analyze potential enabling wide range services, including sharing, offloading caching, attack detection, localization, mobile crowdsensing, security. We then extensive use various key such smart healthcare, transportation, Unmanned Aerial Vehicles (UAVs), cities, industry. important lessons learned review FL-IoT are also highlighted. complete highlighting current challenges possible directions future research booming area.