A Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks in Smart Grid
Bibliographic record
Abstract
Privacy preservation in federated learning (FL) has received considerable attention and many approaches have been proposed. However, these approaches rendered the uploaded gradients invisible to the server, which poses a significant challenge in defending against poisoning attacks. In poisoning attacks, malicious or compromised participants use poisoned training data or forged local updates to disrupt the training process. It is hard for cloud servers to defend against poisoning attacks due to the invisibility of gradients. To address this issue, we propose a privacy-preserving FL scheme (PFLS) against poisoning attacks to eliminate the impact of model poisoning attacks while protecting the privacy of participants. Specifically, a dynamic adaptive defense mechanism is designed to mitigate the impact of malicious gradients and locate malicious participants. To protect participants’ privacy, a multidimensional homomorphic encryption method is constructed with a hierarchical aggregation architecture. The security analysis illustrates that the PFLS scheme can ensure the privacy of FL participants. The experimental results demonstrate that a high-detection rate of malicious participants and a balance between efficiency and robustness are achieved.
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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 | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".