A Scheme for Robust Federated Learning with Privacy-preserving Based on Krum AGR
Bibliographic record
Abstract
The sensitive information of participants would be leaked to an untrustworthy server through gradients in federated learning. Encrypted aggregation of uploaded parameters could resolve this issue. However, it brings challenges to the defense of model poisoning attacks in federated learning while solving the privacy problem. To address this issue, a robust federated learning scheme with privacy-preserving (RFLP) is proposed to eliminate the impact of model poisoning attacks while protecting the privacy of participants against untrusted servers. Specifically, an abnormal gradients detecting method is designed to achieve robust federated learning under encrypted aggregation using Pailliar homomorphic encryption. It is based on the concept of Krum aggregation algorithm (AGR), but utilizes privacy-preserving data features, thereby ensuring privacy. To reduce the rounds of communication in robust aggregation, a multidimensional homomorphic encryption approach is constructed. Besides, an aggregated signature authentication method is also constructed to ensure data integrity during transmission. The experiment results show that the training accuracy of RFLP with 10% malicious participants is 11.9% and 15.3% higher than that without robust aggregation.
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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 | 0.005 |
| 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 | 0.004 |
| 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".