Privacy-preserving federated unsupervised domain adaptation with application to age prediction from DNA methylation data
Bibliographic record
Abstract
MOTIVATION: Generalizing machine learning models across small, high-dimensional, and heterogeneous biological datasets remains a critical challenge due to domain shifts caused by variations in data collection, population differences, and privacy constraints that restrict data sharing. Existing federated domain adaptation (FDA) approaches primarily rely on deep learning and focus on classification tasks, making them unsuitable for privacy-sensitive, small-scale regression problems in biomedical research. We introduce a privacy-preserving federated method for unsupervised domain adaptation in regression, enabling robust learning across distributed, high-dimensional datasets while maintaining full data privacy. RESULTS: Our method is the first to enable distributed training of Gaussian processes for domain adaptation, ensuring complete privacy through randomized encoding and secure aggregation. Unlike deep learning-based FDA approaches, our method is specifically designed for small-scale, high-dimensional biological data, overcoming prior limitations in scalability and generalization. We evaluate our approach on age prediction from DNA methylation data, demonstrating that it achieves performance comparable to non-private state-of-the-art methods while fully preserving data privacy. This work enables secure and effective cross-institutional collaboration in biomedical research without requiring raw data sharing. AVAILABILITY AND IMPLEMENTATION: The source code for our method is available at https://github.com/mdppml/FREDA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".