论文标题
将联合和积极学习结合用于航空的沟通效率分布式故障预测
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
论文作者
论文摘要
近年来，机器学习已被证明是实现工业系统失败预测的一种方式。但是，运行学习算法所需的高计算资源是其广泛应用程序的障碍。分布式学习的子场通过启用远程资源的使用提供了解决此问题的解决方案，但要以不总是可以接受的应用程序中引入通信成本。在本文中，我们提出了一种分布式学习方法，能够优化计算和通信资源的使用，以通过集中式体系结构实现出色的学习模型性能。为了实现这一目标，我们提出了一种新的集中分布式学习算法，该算法依赖于主动学习和联合学习的学习范式，以提供一种沟通效率高效的方法，该方法可为客户和中央服务器提供模型精度保证。我们在公共基准上评估了这种方法，并表明其精确度的性能非常接近最新的非分布学习绩效水平，尽管有其他限制。
Machine Learning has proven useful in the recent years as a way to achieve failure prediction for industrial systems. However, the high computational resources necessary to run learning algorithms are an obstacle to its widespread application. The sub-field of Distributed Learning offers a solution to this problem by enabling the use of remote resources but at the expense of introducing communication costs in the application that are not always acceptable. In this paper, we propose a distributed learning approach able to optimize the use of computational and communication resources to achieve excellent learning model performances through a centralized architecture. To achieve this, we present a new centralized distributed learning algorithm that relies on the learning paradigms of Active Learning and Federated Learning to offer a communication-efficient method that offers guarantees of model precision on both the clients and the central server. We evaluate this method on a public benchmark and show that its performances in terms of precision are very close to state-of-the-art performance level of non-distributed learning despite additional constraints.
