  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.
