Credit-Based Client Selection for Resilient Model Aggregation in Federated Learning
Bibliographic record
Abstract
Federated Learning (FL) has emerged as a revo-lutionary paradigm in the field of machine learning, enabling multiple participants to collaboratively train models without com-promising the privacy of their individual training data. However, the distributed and decentralized nature of FL also exposes it to a diverse array of poisoning threats, wherein adversaries can inject malicious updates to compromise the integrity and accuracy of the global model. To increase FL robustness against model poisoning attacks, this paper proposes a credit-based defense mechanism, named Credit-Based Client Selection (CBCS), where credit scores are assigned to participating clients based on the accuracy and consistency of their historical model updates. The mechanism selectively incorporates reliable clients with higher credit scores into the model aggregation process, while subjecting low-credit clients to thorough scrutiny or exclusion. Through an extensive series of experiments conducted on non-iid image classification datasets, we rigorously evaluate the performance of the CBCS defense mechanism in normal and adversarial scenarios. The results show that CBCS effectively identifies and excludes adversarial clients, maintaining model accuracy in FL. The proposed approach fortifies the resilience of FL systems in the face of adversarial threats and contributes significantly to the safe and trustworthy deployment of FL across diverse domains.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".