Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
This addresses privacy concerns in federated learning for distributed data users, though it is incremental as it builds on existing dimensionality reduction and federated learning methods.
The paper tackles the problem of enabling federated learning without sharing models by integrating dimensionality-reduced data representations, achieving similar accuracy to Federated Averaging and better performance in small-user settings.
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.