联系、合作和自动化工业系统中的联邦学习机会 (Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems)

from arxiv, The paper has been accepted for publication in the IEEE Communications Magazine. The current arXiv contains an additional Appendix that describes the dataset for the setup of Fig. 5

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.

翻译：下一代自主和网络化工业系统(即机器人、车辆、无人驾驶飞机)推动了超可靠、低潜伏通信(URLLC)和计算的发展,这些联网多试剂系统需要快速、通信高效和分布式机器学习(ML),以提供任务关键控制功能。传播的ML技术,包括联合学习(FL),代表了在遥感、通信和学习方面编织一个新兴的多学科研究领域。FL使得分布式无线系统能够不断进行示范培训:FL利用一种合作聚合方法,即通过URLC连接的网络代理器作为分布式学习者,定期交流当地培训的模型参数。文章探讨了下一代网络化工业系统FL的新兴机会。讨论了开放性问题,重点是在连接的自动化车辆和智能制造中合作驾驶。

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