Opportunities of federated learning in connected, cooperative, and automated industrial systems

Savazzi, Stefano; Nicoli, Monica; Bennis, Mehdi; Kianoush, Sanaz; Barbieri, Luca (2021-03-10)
 
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https://doi.org/10.1109/MCOM.001.2000200

Savazzi, Stefano
Nicoli, Monica
Bennis, Mehdi
Kianoush, Sanaz
Barbieri, Luca
Institute of Electrical and Electronics Engineers
10.03.2021

S. Savazzi, M. Nicoli, M. Bennis, S. Kianoush and L. Barbieri, "Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems," in IEEE Communications Magazine, vol. 59, no. 2, pp. 16-21, February 2021, doi: 10.1109/MCOM.001.2000200

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doi:https://doi.org/10.1109/MCOM.001.2000200
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Abstract

Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable low-laten-cy communications (URLLC) and computing. These networked multi-agent systems require fast, communication-efficient, and distributed machine learning (ML) to provide mission-crit-ical control functionalities. Distributed ML techniques, including federated learning (FL), represent a mushrooming multidisciplinary research area weaving together 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.

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