Efficient and Privacy-Preserving Federated Learning Against Poisoning Adversaries
Bibliographic record
Abstract
The ever-growing data scale and increasingly strict privacy restraint have recently drawn extensive attention to federated learning (FL) as a multi-party machine learning paradigm for achieving high-quality model construction without data collection. Nevertheless, uploading local models in FL can still be exploited by adversaries to infer participants' sensitive data. Furthermore, it is possible for malicious participants to manipulate the global model by submitting poisonous local models. To tackle these challenges, this paper proposes an efficient and privacy-preserving federated learning framework against poisoning adversaries, namely ELFL, which can ensure the confidentiality of local models while effectively resisting data poisoning attacks. Specifically, we first design a grouped secure aggregation algorithm, through which the aggregation server can compute the summations of local models inside logic groups but cannot see individual ones. Then, based on grouped aggregations, our poisoning defense mechanism could detect and quickly phase out malicious participants from training candidates. Moreover, the computational complexity of participants is independent of their total number, so it is suitable for large-scale scenes. Detailed security analysis demonstrates the security of ELFL. Experimental results show that ELFL could maintain a high accuracy against representative data poisoning attacks, and its computational and communication overhead is indeed low.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".