Summary of Privacy-preserving Federated Unsupervised Domain Adaptation For Regression on Small-scale and High-dimensional Biological Data, by Cem Ata Baykara et al.
Privacy-Preserving Federated Unsupervised Domain Adaptation for Regression on Small-Scale and High-Dimensional Biological Data
by Cem Ata Baykara, Ali Burak Ünal, Nico Pfeifer, Mete Akgün
First submitted to arxiv on: 26 Nov 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 The paper proposes a federated method called freda, which enables unsupervised domain adaptation in regression tasks for high-dimensional datasets. The method is designed to address challenges in biological data, where data is decentralized and small-scale, and relies on Gaussian Processes to model complex feature relationships while ensuring complete data privacy through randomized encoding and secure aggregation. Unlike existing deep learning-based federated methods, freda achieves performance comparable to centralized state-of-the-art methods while preserving data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary freda is a new way to help machines learn from different types of biological data without seeing the raw data itself. This is important because this type of data is often small and hard to collect, and scientists need to keep it private. The method uses something called Gaussian Processes to understand how features are related, and ensures that all data stays safe by using random codes and secure math. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation » Regression » Unsupervised
