An Enhanced Combinatorial Contextual Neural Bandit Approach for Client Selection in Federated Learning
Bibliographic record
Abstract
In the evolving landscape of machine learning (ML), federated learning (FL) stands out as an innovative strategy for training models across dispersed devices without centralizing raw data. Such an approach, however, grapples with data heterogeneity challenges, violating the independent and identically distributed (IID) assumption and undermining the global model accuracy. To address this, we present federated adversary-resilient neural selector (FANS), a sophisticated context-aware client selection algorithm, leveraging a combinatorial contextual neural bandit framework. This algorithm that accentuates the enhanced extraction of contextual information by evaluating each local client with a universally standardized dataset, subsequently yielding a more insightful contextual representation tailored for federated settings. In addition, we introduce selection robustness score (SRS), a novel metric designed to quantify the efficacy of client selection in the presence of adversarial 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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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.005 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".