# FED-chi2: Privacy Preserving Federated Correlation Test

arXiv: https://arxiv.org/abs/2105.14618 · HTML: https://mlsanity.com/paper/2105.14618/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2021.

## Built on by (most cited first, 2 of 2)

- [Towards Practical Federated Causal Structure Learning](https://mlsanity.com/paper/2306.09433/) (2023)
- [Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding](https://mlsanity.com/paper/2603.05149/) (2026)

## Abstract (arXiv)

In this paper, we propose the first secure federated chi2-test protocol Fed-chi2. To minimize both the privacy leakage and the communication cost, we recast chi2-test to the second moment estimation problem and thus can take advantage of stable projection to encode the local information in a short vector. As such encodings can be aggregated with only summation, secure aggregation can be naturally applied to hide the individual updates. We formally prove the security guarantee of Fed-chi2 that the joint distribution is hidden in a subspace with exponential possible distributions. Our evaluation results show that Fed-chi2 achieves negligible accuracy drops with small client-side computation overhead. In several real-world case studies, the performance of Fed-chi2 is comparable to the centralized chi2-test.