# Scalable and Privacy-Preserving Federated Principal Component Analysis

arXiv: https://arxiv.org/abs/2304.00129 · HTML: https://mlsanity.com/paper/2304.00129/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [Decentralized Differentially Private Power Method](https://mlsanity.com/paper/2507.22849/) (2025)
- [Rapid Grassmannian Averaging with Chebyshev Polynomials](https://mlsanity.com/paper/2410.08956/) (2024)
- [Federated Structured Sparse PCA for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2503.23981/) (2025)
- [PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework](https://mlsanity.com/paper/2509.21704/) (2025)
- [Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network](https://mlsanity.com/paper/2604.09091/) (2026)

## Abstract (arXiv)

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confidentiality. Our solution, SF-PCA, is an end-to-end secure system that preserves the confidentiality of both the original data and all intermediate results in a passive-adversary model with up to all-but-one colluding parties. SF-PCA jointly leverages multiparty homomorphic encryption, interactive protocols, and edge computing to efficiently interleave computations on local cleartext data with operations on collectively encrypted data. SF-PCA obtains results as accurate as non-secure centralized solutions, independently of the data distribution among the parties. It scales linearly or better with the dataset dimensions and with the number of data providers. SF-PCA is more precise than existing approaches that approximate the solution by combining local analysis results, and between 3x and 250x faster than privacy-preserving alternatives based solely on secure multiparty computation or homomorphic encryption. Our work demonstrates the practical applicability of secure and federated PCA on private distributed datasets.