# Federated Data Analytics: A Study on Linear Models Source: [Federated Data Analytics: A Study on Linear Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:29:17.533Z Authors: Xubo Yue, Raed Al Kontar, Ana Mar\\'ia Estrada G\\'omez Published: 2022-06-15T19:50:07.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Core Speed Prediction on C-MAPSS (30% RUL)](): Core Speed Prediction; C-MAPSS (30% RUL); RMSE: 15.2. - [Core Speed Prediction on C-MAPSS (10% RUL)](): Core Speed Prediction; C-MAPSS (10% RUL); RMSE: 7.3. - [Reinforcement Learning on MuJoCo Hopper target planet (Venus-like gravity)](): Reinforcement Learning; MuJoCo Hopper target planet (Venus-like gravity); Reward: 2666. - [Core Speed Prediction on C-MAPSS 90% RUL](): Core Speed Prediction; C-MAPSS 90% RUL; RMSE: 21.4. - [Core Speed Prediction on C-MAPSS (70% RUL)](): Core Speed Prediction; C-MAPSS (70% RUL); RMSE: 35.2. - [Core Speed Prediction on C-MAPSS 50% RUL](): Core Speed Prediction; C-MAPSS 50% RUL; RMSE (Core Speed): 24.9. ## Original sources and implementation [Original paper]() [PDF]()