Federated Data Analytics: A Study on Linear Models

TL;DR

This paper develops federated data analytics methods for linear regression models, enabling decentralized data processing with uncertainty quantification, variable selection, and fast adaptation, validated on real-world applications like aircraft engine monitoring.

Contribution

Introduces federated hierarchical linear models that facilitate information sharing and provide comprehensive statistical inference in a decentralized setting.

Findings

Models perform well on real-life applications

Frameworks enable uncertainty quantification and variable selection

Serve as competitive benchmarks for federated algorithms

Abstract

As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process more of the data locally. This regime of analytics is coined as federated data analytics (FDA). In spite of the recent success stories of FDA, most literature focuses exclusively on deep neural networks. In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression. Our treatment is built upon hierarchical modeling that allows borrowing strength across multiple groups. To this end, we propose two federated hierarchical model structures that provide a shared representation across devices to facilitate information sharing. Notably, our proposed frameworks are capable of providing uncertainty quantification, variable selection, hypothesis testing and fast adaptation to new unseen data. We validate our methods on a range of real-life applications including condition monitoring for aircraft engines. The results show that our FDA treatment for linear models can serve as a competing benchmark model for future development of federated algorithms.