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.

中文翻译：

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将联合学习和主动学习相结合，实现航空通信高效的分布式故障预测

近年来，机器学习已被证明是一种实现工业系统故障预测的方法。然而，运行学习算法所需的大量计算资源是其广泛应用的障碍。分布式学习的子领域通过启用远程资源的使用为这个问题提供了解决方案，但代价是在应用程序中引入了并不总是可接受的通信成本。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，通过集中式架构实现出色的学习模型性能。为了达成这个，我们提出了一种新的集中式分布式学习算法，该算法依赖于主动学习和联合学习的学习范式，以提供一种高效通信的方法，该方法可以保证客户端和中央服务器上的模型精度。我们在公共基准上评估了这种方法，并表明尽管存在额外的限制，但其在精度方面的性能非常接近非分布式学习的最新性能水平。