aiqbench analysis
FedPS addresses a critical bottleneck in Federated Learning by enabling efficient, privacy-preserving data preprocessing through aggregated statistics rather than raw data sharing. This framework significantly reduces communication overhead while ensuring consistent feature scaling and imputation across distributed nodes, directly enhancing the practicality of large-scale FL deployments. The work advances machine learning infrastructure by solving long-overlooked challenges in handling heterogeneous data formats without compromising model performance.
FedPS: Federated Preprocessing for structured data via aggregated Statistics
arXiv:2602.10870v2 Announce Type: replace-cross 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 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.
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 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.
TMLR 2026. 27 pages, 8 figures, 7 tables. Project page see this http URL
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2602.10870 [cs.LG]
(or arXiv:2602.10870v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.10870 Focus to learn more arXiv-issued DOI via DataCite
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