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

• URL: http://arxiv.org/abs/2101.03367v2
• Date: Tue, 12 Jan 2021 22:42:24 GMT
• Title: Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems
• Authors: Stefano Savazzi, Monica Nicoli, Mehdi Bennis, Sanaz Kianoush, Luca Barbieri
• Abstract summary: Next-generation industrial systems have driven advances in ultra-reliable, low latency communications. Distributed machine learning (FL) represents a mushrooming multidisciplinary research area weaving in sensing, communication and learning. This article explores emerging opportunities of FL for the next-generation networked industrial systems.
• Score: 44.627847349764664
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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