FedPS: Federated data Preprocessing via aggregated Statistics
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.
698d93af65c0d15a6d162312
Xuefeng Xu
claimed_verified
2026-02-12T13:25:53.187Z
65d836973381a5be278a475a
https://cdn-avatars.huggingface.co/v1/production/uploads/65d836973381a5be278a475a/EAjHxXgSrZ_CY67d-zF1I.jpeg
Xuefeng Xu
xuefeng-xu
user
xuefeng-xu
698d93af65c0d15a6d162313
Graham Cormode
FedPS: Federated data Preprocessing via aggregated Statistics
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.
FedPS is a federated data preprocessing framework that uses aggregated statistics and data-sketching techniques to enable efficient and privacy-preserving data preparation for collaborative machine learning across distributed datasets.
federated learning data preprocessing data-sketching aggregated statistics feature scaling encoding discretization missing-value imputation k-Means k-Nearest Neighbors Bayesian Linear Regression horizontal FL vertical FL