FedPS: Federated data Preprocessing via aggregated Statistics

TL;DR

FedPS is a federated data preprocessing framework that uses aggregated statistics and data-sketching techniques to enable privacy-preserving, communication-efficient, and consistent data preprocessing for federated learning systems.

Contribution

This paper introduces FedPS, a novel federated preprocessing framework that leverages data sketches for efficient, privacy-preserving data summarization and extends preprocessing models to federated settings.

Findings

FedPS achieves efficient data summarization with privacy preservation.

It supports multiple preprocessing tasks like scaling, encoding, and imputation.

FedPS extends models such as k-Means and KNN to federated learning environments.

Abstract

Federated Learning (FL) enables multiple parties to collaboratively train machine learning models without sharing raw data. However, before training, data must be preprocessed to address missing values, inconsistent formats, and heterogeneous feature scales. This preprocessing stage is critical for model performance but is largely overlooked in FL research. In practical FL systems, privacy constraints prohibit centralizing raw data, while communication efficiency introduces further challenges for distributed preprocessing. We introduce FedPS, a unified framework for federated data preprocessing based on aggregated statistics. FedPS leverages data-sketching techniques to efficiently summarize local datasets while preserving essential statistical information. Building on these summaries, we design federated algorithms for feature scaling, encoding, discretization, and missing-value imputation, and extend preprocessing-related models such as k-Means, k-Nearest Neighbors, and Bayesian Linear Regression to both horizontal and vertical FL settings. FedPS provides flexible, communication-efficient, and consistent preprocessing pipelines for practical FL deployments.