VFLR: An Efficient and Privacy-Preserving Vertical Federated Framework for Logistic Regression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the explosive growth of data volume and computing capability, federated learning, which involves constructing global models over multiple data islands, has demonstrated its advantages and vast prospects in the field of machine learning. However, due to commonly vertically partitioned data, coupled with privacy concerns about data leakage, there are still some challenging issues in traditional federated learning. To tackle these challenges, in this article, we propose an efficient and privacy-preserving vertical federated learning framework for logistic regression, named VFLR, where multiple participants can collaboratively perform global model training and query over their vertically partitioned data. Specifically, we first design a data aggregation matrix construction algorithm, with which the vertically partitioned data can be aggregated for high-accuracy global model training. Then, by utilizing a novel symmetric homomorphic encryption, our framework can ensure that the whole training and query processes do not leak any private information. Moreover, based on the data aggregation matrix, multi-round interactions are not required in VFLR, improving training efficiency significantly. Detailed security analysis shows that VFLR can well protect data and model information from inference attacks. In addition, extensive experiments demonstrate that VFLR has high training and query accuracy and low computation and communication overhead.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it