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

Signal Processing