Owen Sampling Accelerates Contribution Estimation in Federated Learning
Bibliographic record
Abstract
Federated Learning (FL) aggregates information from multiple clients to train a shared global model without exposing raw data. Accurately estimating each client’s contribution is essential not just for fair rewards, but for selecting the most useful clients so the global model converges faster. The Shapley value is the principled choice for this, yet exact computation scales exponentially with the number of clients, making it infeasible for real-world FL deployments with many participants. In this paper, we propose FedOwen, an efficient federated contribution evaluation framework adopting Owen sampling to approximate Shapley values under the same total evaluation budget as the existing methods, while keeping the approximation error below a small threshold. In addition, FedOwen applies an adaptive client selection strategy that balances exploiting high-value clients with exploring under-sampled ones, avoiding bias toward a narrow subset, and uncovering rare but informative data. Under a fixed valuation cost, FedOwen achieves up to 23% improvement in final model accuracy within the same number of communication rounds, compared to state-of-the-art baselines on non-IID benchmarks. Code: https://github.com/hoseinkhs/AdaptiveSelectionFL [17]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".