Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges

The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have successful applications IoT the industry. For example, transportation systems, novel vehicles (IoV) enabled new research directions and automation solutions. Moreover, reinforcement learning (RL), federated (FL), (FRL) demonstrated remarkable success solving complex problems different applications. In recent years, solutions developed based on this combined framework (i.e., learning). Conversely, there is lack analysis concerning standard view challenges future current FRL landscape. Thereupon, main goal to present literature review from multiple perspectives. We focus analyzing areas (e.g., security, sustainability efficiency, vehicular solutions, industrial services) highlight existing their characteristics, gaps. Additionally, we identify key short- long-term leading opportunities field. This intends picture ecosystem foster development challenges.