  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.
