A Max-Min Security Game for Coordinated Backdoor Attacks on Federated Learning
Bibliographic record
Abstract
We address in this paper the challenge of data poisoning attacks on Federated Learning. We consider a particularly challenging attack scenario in which a single poisoning attack is coordinated over a set of clients to complicate its detection. In response, the federated learning server assigns a weight to each client’s model update with the aim of mitigating the effects of the poisoning on the global model. To address this challenge, we first design a trust mechanism that enables the federated learning server to assess the trustworthiness of each client on the basis of the client’s adherence to the federated learning protocol and the quality of data contributed by the client. Capitalizing on the trust mechanism, we model the interactions between the attacker and federated learning server as a security max-min game. The outcome of the game guides the server on the optimal weight assignment strategy over the set of clients’ model updates, so as to minimize the effects of the data poisoning on the global model. Simulations conducted on the MNIST and CIFAR-10 datasets suggest that our proposed solution decreases the coordinated attack success rate, as well as the false positive and false negative percentages compared to two baseline solutions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".