Privacy-Preserving Federated Learning Secure Aggregation Strategy Based on Permissioned Blockchain
Bibliographic record
Abstract
In order to solve the problems of privacy-preserving federated learning in which the poisoning behaviour of client nodes as well as the malicious aggregation behaviour of aggregation server nodes lead to the failure of model training, a secure aggregation privacy-preserving federated learning strategy based on permissioned blockchain is proposed. In order to solve the problem of malicious behavior of aggregation server nodes, a trusted aggregation server node selection algorithm is designed to cancel the right of nodes with malicious aggregation behavior to participate in the aggregation server node election. Each node establishes a reputation value, proposes a reward and punishment algorithm based on the reputation value, and establishes a threshold, and nodes with a reputation value lower than the threshold will be refused to participate in the federated learning process to reduce the threat of client node poisoning attack behaviour on model learning.The experimental results show that the scheme is able to ensure secure model aggregation and achieve high model correctness in the presence of malicious aggregation behaviour at 50% of the nodes and poisoning attacks at 40% of the nodes.
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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 | 0.001 | 0.003 |
| 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".