Summary of Federated Block-term Tensor Regression For Decentralised Data Analysis in Healthcare, by Axel Faes et al.
Federated Block-Term Tensor Regression for decentralised data analysis in healthcare
by Axel Faes, Ashkan Pirmani, Yves Moreau, Liesbet M. Peeters
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to block-term tensor regression (BTTR) for modeling complex, high-dimensional data. The traditional BTTR method has shown promise in applications like healthcare and neuroscience, but it relies on centralized datasets, which raises concerns about privacy and collaboration across institutions. To overcome these limitations, the authors introduce Federated Block-Term Tensor Regression (FBTTR), a modification of BTTR designed for federated learning scenarios. FBTTR enables decentralized data analysis, allowing institutions to build predictive models while preserving data privacy and complying with regulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to analyze big data. It’s called Federated Block-Term Tensor Regression (FBTTR), which helps doctors and scientists understand complex patterns in healthcare and neuroscience data. The problem is that most data is kept private, so it’s hard for different institutions to work together without sharing sensitive information. FBTTR solves this by letting institutions keep their own data private while still working together to create better models. |
Keywords
» Artificial intelligence » Federated learning » Regression
