# Towards federated multivariate statistical process control (FedMSPC)

arXiv: https://arxiv.org/abs/2211.01645 · HTML: https://mlsanity.com/paper/2211.01645/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)

## Built on by (most cited first, 3 of 3)

- [Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation](https://mlsanity.com/paper/2307.11756/) (2023)
- [Federated Block-Term Tensor Regression for decentralised data analysis in healthcare](https://mlsanity.com/paper/2412.06815/) (2024)
- [P3LS: Partial Least Squares under Privacy Preservation](https://mlsanity.com/paper/2401.14884/) (2024)

## Abstract (arXiv)

The ongoing transition from a linear (produce-use-dispose) to a circular economy poses significant challenges to current state-of-the-art information and communication technologies. In particular, the derivation of integrated, high-level views on material, process, and product streams from (real-time) data produced along value chains is challenging for several reasons. Most importantly, sufficiently rich data is often available yet not shared across company borders because of privacy concerns which make it impossible to build integrated process models that capture the interrelations between input materials, process parameters, and key performance indicators along value chains. In the current contribution, we propose a privacy-preserving, federated multivariate statistical process control (FedMSPC) framework based on Federated Principal Component Analysis (PCA) and Secure Multiparty Computation to foster the incentive for closer collaboration of stakeholders along value chains. We tested our approach on two industrial benchmark data sets - SECOM and ST-AWFD. Our empirical results demonstrate the superior fault detection capability of the proposed approach compared to standard, single-party (multiway) PCA. Furthermore, we showcase the possibility of our framework to provide privacy-preserving fault diagnosis to each data holder in the value chain to underpin the benefits of secure data sharing and federated process modeling.