EPPS: Efficient Privacy-Preserving Scheme in Distributed Deep Learning
Bibliographic record
Abstract
As a promising training model with Neural Network, distributed deep learning has been widely applied in various scenarios, where clients and the cloud server work together only by sharing local gradients and global parameters. However, research has shown that the adversary can still reconstruct the users' private information even if little information is leaked. To address this problem, several approaches of privacy-preserving distributed training have been exploited with existing mature technologies, such as Differential Privacy, Secure Multi-party Computation and Homomorphic Encryption. However, state of-the-art results are still defective in security, functionality and efficiency. In this paper, we propose an Efficient Privacy Preserving Scheme (EPPS) for distributed deep learning. We claim that our solution achieves the best performance tradeoff between security, efficiency and functionality. Specifically, we adopt the threshold Paillier encryption as the underlying structure to construct our secure training model. Hence, the confidentiality of honest users' of local gradients can be guaranteed, even the cloud server colluding with multiple users. In addition, since users are often accidentally offline due to either network environment or equipment damage, our EPPS can also support users exiting at any phases of the entire work process. Further more, we conducted extensive experiments on real-world data to demonstrate the preferable performance of our proposed scheme.
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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 | 0.004 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".