Conformal Data Contamination Tests for Trading or Sharing of Data

TL;DR

This paper introduces a distribution-free, conformal testing framework to identify contaminated external data, ensuring quality guarantees for data sharing in machine learning without relying on distributional assumptions.

Contribution

It proposes novel conformal data contamination tests that provide rigorous, distribution-free quality guarantees for external data in collaborative learning scenarios.

Findings

Tests remain valid under arbitrary contamination levels

Effective in diverse collaborative learning scenarios

Enables false discovery rate control

Abstract

The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can be expanded through trading or sharing with external data agents. However, data buyers need quality guarantees before purchasing, as external data may be contaminated or irrelevant to their specific learning task. Previous works primarily rely on distributional assumptions about data from different agents, relegating quality checks to post-hoc steps involving costly data valuation procedures. We propose a distribution-free, contamination-aware data-sharing framework that identifies external data agents whose data is most valuable for model personalization. To achieve this, we introduce novel two-sample testing procedures, grounded in rigorous theoretical foundations for conformal outlier detection, to determine whether an agent's data exceeds a contamination threshold. The proposed tests, termed conformal data contamination tests, remain valid under arbitrary contamination levels while enabling false discovery rate control via the Benjamini-Hochberg procedure. Empirical evaluations across diverse collaborative learning scenarios demonstrate the robustness and effectiveness of our approach. Overall, the conformal data contamination test distinguishes itself as a generic procedure for aggregating data with statistically rigorous quality guarantees.