# FedPS: Federated Preprocessing for structured data via aggregated Statistics

arXiv: https://arxiv.org/abs/2602.10870 · HTML: https://mlsanity.com/paper/2602.10870/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [CleanAgent: Automating Data Standardization with LLM-based Agents](https://mlsanity.com/paper/2403.08291/) (2024)
- [Optimal Quantile Approximation in Streams](https://mlsanity.com/paper/1603.05346/) (2016)
- [SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning](https://mlsanity.com/paper/2210.01639/) (2022)
- [Relative Error Streaming Quantiles](https://mlsanity.com/paper/2004.01668/) (2020)
- [A High-Performance Algorithm for Identifying Frequent Items in Data Streams](https://mlsanity.com/paper/1705.07001/) (2017)
- [Power Transform Revisited: Numerically Stable, and Federated](https://mlsanity.com/paper/2510.04995/) (2025)
- [Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach](https://mlsanity.com/paper/2511.11249/) (2025)

## Abstract (arXiv)

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 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 Bayesian Linear Regression to both horizontal and vertical FL settings. FedPS provides flexible, communication-efficient, and consistent preprocessing pipelines for practical FL deployments.