论文标题
工业联邦学习 - 需求和系统设计
Industrial Federated Learning -- Requirements and System Design
论文作者
论文摘要
联合学习（FL）是一种非常有前途的方法，可以通过在没有透露私人数据的情况下交换参与客户之间的知识来改善分散的机器学习（ML）模型。然而，由于假定所有FL任务都假定强大的数据相似性，但FL仍未针对工业环境量身定制。在工业机器数据中，这种情况很少有机器类型，操作和环境条件的变化。因此，我们引入了一个工业联盟学习（IFL）系统，该系统在连续评估和更新的学习任务中支持知识交流，并具有足够的数据相似性。这使得在常见的ML问题中对业务合作伙伴的最佳合作，防止负面知识转移，并确保对涉及边缘设备的资源优化。
Federated Learning (FL) is a very promising approach for improving decentralized Machine Learning (ML) models by exchanging knowledge between participating clients without revealing private data. Nevertheless, FL is still not tailored to the industrial context as strong data similarity is assumed for all FL tasks. This is rarely the case in industrial machine data with variations in machine type, operational- and environmental conditions. Therefore, we introduce an Industrial Federated Learning (IFL) system supporting knowledge exchange in continuously evaluated and updated FL cohorts of learning tasks with sufficient data similarity. This enables optimal collaboration of business partners in common ML problems, prevents negative knowledge transfer, and ensures resource optimization of involved edge devices.
