Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems

TL;DR

This paper explores how federated learning can enhance communication efficiency and real-time control in next-generation industrial systems like autonomous vehicles and robots, highlighting opportunities and open challenges.

Contribution

It provides a comprehensive overview of federated learning applications in industrial systems, emphasizing new opportunities and discussing open research problems.

Findings

Federated learning enables efficient, distributed model training in industrial multi-agent systems.

FL reduces communication overhead compared to centralized data aggregation.

Open problems include cooperative driving and collaborative robotics challenges.

Abstract

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.