Efficiently Achieving Privacy Preservation and Poisoning Attack Resistance in Federated Learning
Bibliographic record
Abstract
Federated learning enables clients to train models locally and provide local updates to the server instead of raw dataset, thereby preserving data privacy to some extent. However, adversaries can still pry users’ privacy by inferring updates, and compromise the integrity of the global model through poisoning attack. Therefore, many related works have integrated poisoning attack detection method with secure computation to address both issues. Nevertheless, they still encounter two major challenges: (i) the efficiency is too low to be applied in practice, and (ii) the privacy is still at risk of being leaked, e.g., the distance of two local updates for detecting poisoning attack could be exposed to the server. Aiming at the challenges, in this paper, we propose an Efficient Privacy-preserving and Poisoning attack Resistant scheme for Federated Learning, named EPPRFL, which preserves the privacy for local updates and some intermediate information used to detect poisoning attack. In particular, we design an efficient poisoning attack detection method based on Euclidean distance filtering & clipping technique, named F&C. Then, considering the privacy preservation of the F&C method, we efficiently customize secure comparison, secure median, secure distance computation and secure clipping protocols based on additive secret sharing. Experimental results and theoretical analysis show that compared with existing schemes, EPPRFL can better resist poisoning attack and has lower computational and communication overheads on the client side.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".