Federated generalized scalar-on-tensor regression

Abstract

AbstractComplex systems are generating more and more high-dimensional data for which tensor analysis showed promising results by capturing complex correlation structures of data. Such data is often distributed among various sites creating challenges for developing data-driven models. Specifically, data privacy and security concerns have been exacerbated in recent years and drove the demand to store and analyze data at the edge of networks rather than sharing it with a centralized server. Federated learning frameworks have been introduced as a solution to these concerns. These frameworks allow local clients to learn local models and collaborate with others to develop a more generalizable aggregated model while handling data privacy issues. In this article, we propose a federated generalized scalar-on-tensor regression framework where multiple local tensor models are learned at the edge, and their parameters are shared with and updated by an aggregator. Experiments on synthetic data sets and two real-world data sets from agriculture and manufacturing domains show the superiority of our approach over several benchmarks.Keywords: aggregated modelfederated learningpersonalized modelscalar-on-tensor regression AcknowledgementsWe would like to thank Ioannis Ampatzidis, Lucas Fideles Costa and Vitor Gontijo da Cunha for providing hyperspectral image data collected at the Southwest Florida Research and Education Center. Also, we would like to thank Massimo Pacella for providing access to the vehicle engine sensor data.Data availability statementThe data used in this article are not publicly available. To request access to the data used in Case Study I (Section 6.1) and Case Study II (Section 6.2), one may contact the corresponding authors of (Costa et al. Citation2022) and (Pacella Citation2018), respectively.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work has been partially supported by the National Science Foundation (NSF) award 2212878.Notes on contributorsElif KonyarElif Konyar is a doctoral student in the Department of Industrial and Systems Engineering at University of Florida. Her email address is elif.konyar@ufl.edu.Mostafa Reisi GahrooeiDr. Mostafa Reisi Gahrooei is an Assistant Professor in the Department of Industrial and Systems Engineering at University of Florida. His email address is mreisigahrooei@ufl.edu. He is the corresponding author.