Fast Generation-Based Gradient Leakage Attacks: An Approach to Generate Training Data Directly From the Gradient
Bibliographic record
Abstract
Federated learning (FL) is a distributed machine learning technique that guarantees the privacy of user data. However, FL has been shown to be vulnerable to gradient leakage attacks (GLA), which have the ability to reconstruct private training data from public gradients with high probability. These attacks are either analytic-based, requiring modification of the FL model, or optimization-based, requiring long convergence times and failing to effectively address the challenge of dealing with highly compressed gradients in practical FL systems. This paper presents a pioneering generation-based GLA method called FGLA that can reconstruct batches of user data without the need for the optimization process. We specifically design a feature separation technique that first extracts the features of each sample in a batch and then directly generates the user data. Our extensive experiments on multiple image datasets show that FGLA can reconstruct user images in seconds with a batch size of 256 from highly compressed gradients (0.8% compression ratio or higher), thereby significantly outperforming state-of-the-art methods.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".