Toward Fair and Efficient Neural Architecture Search in Heterogeneous Federated Learning
Bibliographic record
Abstract
Federated learning (FL) enables collaborative model training across distributed data silos, but non-independent and identically distributed (non-IID) heterogeneity among clients often leads to biased or underperforming global models. Neural Architecture Search (NAS) can adapt model structures to diverse data distributions, yet conventional two-stage search-retrain pipelines remain prohibitively expensive for federated settings. We propose OSMONAS (One-Stage Meta-Optimization NAS), a unified framework that integrates Model-Agnostic Meta-Learning (MAML) with differentiable NAS and a progressive soft pruning strategy. OSMONAS jointly optimizes model weights and architectures within a single-stage search, accelerating convergence while preserving performance stability. The integrated pruning mechanism removes redundant operations during search, eliminating the need for post-hoc retraining. Evaluations on standard benchmarks under varying non-IID conditions demonstrate that OSMONAS consistently achieves higher accuracy and faster convergence than state-of-the-art baselines. Beyond computational gains, OSMONAS offers a pathway toward equitable and inclusive federated healthcare, where adaptable architectures enable collaboration across institutions with diverse data, devices, and resources.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".