Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems
Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems
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DOI：
10.1109/mcom.001.2000200
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发表时间：
2021-02-01
影响因子：
11.2
通讯作者：
Barbieri, Luca
中科院分区：
文献类型：
--
作者：
Savazzi, Stefano;Nicoli, Monica;Barbieri, Luca
下一代自主和网络工业系统（即机器人、车辆、无人机）推动了超可靠低延迟通信 (URLLC) 和计算的进步。这些网络多代理系统需要快速、通信高效的分布式机器学习 (ML) 来提供关键任务控制功能。分布式机器学习技术，包括联邦学习 (FL)，代表了一个蓬勃发展的多学科研究领域，将传感、通信和学习融为一体。 FL 支持分布式无线系统中的持续模型训练：FL 利用协作融合方法，而不是在集中式服务器上融合原始数据样本，其中通过 URLLC 连接的网络代理充当分布式学习器，定期交换其本地训练的模型参数。本文探讨了 FL 在下一代网络工业系统中的新兴机遇。讨论了开放性问题，重点关注联网自动驾驶车辆中的协作驾驶和智能制造中的协作机器人。
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.