  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.
