Communication-Aware Optimizers for FedMeta Learning in Aerial Wireless Networks
Bibliographic record
Abstract
Federated Meta (FedMeta) learning integrates meta learning with Federated Learning (FL) towards addressing heterogeneity challenges in edge intelligence. Implementing FedMeta on uncrewed aerial vehicles (UAVs)-assisted aerial networks offers compound benefits, including optimal trajectory design and resource allocation. The quick adaptability of FedMeta to new edge devices with minimal data is challenged in adverse UAV-based wireless networks due to frequent update losses that lead to model bias and overfitting towards data from devices with better channels. In this letter, we propose an optimizer for the global FedMeta model update that is suitable for adverse channel conditions. We show that the proposed optimizer outperforms the state-of-the-art AdamW optimizer for FedMeta learning. By leveraging tools from stochastic geometry, particularly in UAV-orchestrated networks, we gain insights into channel behavior and integrate them into our algorithm. We also introduce a novel hybrid update rule combining our optimized strategy with AdamW, achieving superior convergence speed and overall accuracy. Extensive simulations on LEAF datasets under unreliable channel conditions validate the effectiveness of our methods.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".