Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an alternative federated learning system that enables integration of dimensionality reduced representations of distributed data prior to a supervised learning task, thus avoiding model sharing among the parties. We compare the performance of this approach on image classification tasks to three alternative frameworks: centralized machine learning, individual machine learning, and Federated Averaging, and analyze potential use cases for a federated learning system without model sharing. Our results show that our approach can achieve similar accuracy as Federated Averaging and performs better than Federated Averaging in a small-user setting.

本文探讨了一种替代联合学习系统，它可以在监督学习任务之前对分布式数据进行降维处理，从而避免了各方之间的模型共享。作者比较了此方法在图像分类任务中与三个替代框架：集中式机器学习、个体机器学习和联合平均学习的性能，并分析了没有模型共享的联合学习系统的潜在用例。结果表明，该方法可以实现与联合平均学习相似的准确性，并在小用户设置中表现优于联合平均学习。

通过降维数据表示整合的无模型共享联邦学习系统