联邦学习中用统计汇总实现高效隐私保护预处理
FedPS: Federated data Preprocessing via aggregated Statistics
- 通过数据摘要技术聚合本地统计信息，避免原始数据共享
- 支持特征缩放、缺失值填补等四类预处理操作
- 适用于水平与垂直联邦学习，兼顾效率与一致性
联邦学习（FL）允许多方在不共享原始数据的情况下协同训练模型。然而，在训练前需进行数据预处理以解决缺失值、格式不一致和特征尺度异构等问题。这一阶段对模型性能至关重要，但在现有联邦学习研究中被严重忽视。实际应用中，隐私限制禁止集中原始数据，通信效率又增加了分布式预处理的难度。本文提出FedPS，一种基于聚合统计的统一联邦预处理框架。该框架利用数据摘要技术高效总结本地数据集，同时保留关键统计信息。基于这些摘要，设计了用于特征缩放、编码、离散化和缺失值插补的联邦算法，并将k-Means、k-近邻和贝叶斯线性回归等预处理相关模型扩展至水平与垂直联邦学习场景。FedPS为实际联邦学习部署提供了灵活、高效且一致的预处理流程。
原文摘要 · Abstract (English)
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.
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