An Enhanced Combinatorial Contextual Neural Bandit Approach for Client Selection in Federated Learning
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Bibliographic record
Abstract
In the dynamic realm of machine learning (ML), federated learning (FL) emerges as a pivotal method for training models on decentralized devices without the need for central data aggregation. This technique confronts the challenges of data heterogeneity, which disrupts the independent and identically distributed (IID) assumptions, adversely affecting the accuracy of the overall model. To tackle this issue, we introduce the federated non-performing-node-resilient neural selector (FNNS), an advanced client selection algorithm grounded in a combinatorial contextual neural bandit framework. This algorithm enhances the extraction of contextual data by assessing each local client using a universally standardized dataset, thereby providing a deeper, context-specific insight suitable for federated environments. In addition, we introduce selection robustness score (SRS), a novel metric designed to quantify the efficacy of client selection in the presence of non-performing-nodes (NPN) conditions. Using this metric, we demonstrate FANS’s effectiveness in enhancing the FL process. Empirical evaluations across diverse settings reveal our method’s superiority over current state-of-the-art solutions, with significant improvements in both SRS and global model accuracy.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it