# Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations Source: [Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:43:58.904Z Authors: Anna Bogdanova, Akie Nakai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai Published: 2020-11-13T08:12:00.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()