Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems
This paper identifies potential applications of federated learning for improving the efficiency and autonomy of industrial systems, which is relevant for researchers and practitioners in distributed machine learning and industrial automation.
This paper explores the opportunities of federated learning (FL) for next-generation networked industrial systems, such as connected automated vehicles and collaborative robotics. It discusses how FL enables continual model training in distributed wireless systems by exchanging locally trained model parameters rather than raw data.
Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing. These networked multi-agent systems require fast, communication-efficient and distributed machine learning (ML) to provide mission critical control functionalities. Distributed ML techniques, including federated learning (FL), represent a mushrooming multidisciplinary research area weaving in sensing, communication and learning. FL enables continual model training in distributed wireless systems: rather than fusing raw data samples at a centralized server, FL leverages a cooperative fusion approach where networked agents, connected via URLLC, act as distributed learners that periodically exchange their locally trained model parameters. This article explores emerging opportunities of FL for the next-generation networked industrial systems. Open problems are discussed, focusing on cooperative driving in connected automated vehicles and collaborative robotics in smart manufacturing.