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首页> 外文期刊>IEEE Communications Magazine >Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems
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Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems

机译：联合，合作社和自动化工业系统联合学习的机遇

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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.
机译：下一代自主和联网工业系统（即机器人，车辆，无人机）在超可靠的低Laten-CY通信（URIFC）和计算中具有驱动的进步。这些网络化的多代理系统需要快速，通信高效和分布式机器学习（ML）以提供任务攻击 - ICL-ICL控制功能。分布式ML技术，包括联合学习（FL），代表了一种蘑菇多学科研究区域，编织传感，沟通和学习。 FL支持分布式无线系统的持续模型培训：而不是在集中式服务器上融合原始数据样本，利用了通过URLLC连接的网络代理作为分布式学习者来定期交换其本地培训的模型参数的合作融合方法。本文探讨了下一代网络工业系统的流动的新兴机会。讨论了打开问题，专注于在智能制造中的连接自动车辆和协作机器人中的合作驾驶。

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