# Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations

arXiv: https://arxiv.org/abs/2011.06803 · HTML: https://mlsanity.com/paper/2011.06803/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Collaborative causal inference on distributed data](https://mlsanity.com/paper/2208.07898/) (2022)
- [Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach](https://mlsanity.com/paper/2501.12723/) (2025)
- [Privacy-preserving recommender system using the data collaboration analysis for distributed datasets](https://mlsanity.com/paper/2406.01603/) (2024)
- [Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings](https://mlsanity.com/paper/2308.00280/) (2023)
- [Estimation of conditional average treatment effects on distributed confidential data](https://mlsanity.com/paper/2402.02672/) (2024)
- [Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data](https://mlsanity.com/paper/2601.09304/) (2026)
- [Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis](https://mlsanity.com/paper/2605.27219/) (2026)
- [Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration](https://mlsanity.com/paper/2212.03373/) (2022)

## Abstract (arXiv)

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.