Clustered and Scalable Federated Learning Framework for UAV Swarms
Bibliographic record
Abstract
Federated learning (FL) has emerged as a machine learning (ML) paradigm for distributed training without requiring trainers to share private data. Coevally, unmanned aerial vehicles (UAVs) become sufficiently powerful that they can contribute to training sophisticated ML models. Direct application of the conventional FL framework to a UAV swarm, however, could result in unnecessarily high communication and computation complexity. This paper aims to address this challenge by proposing a scalable and clustered FL framework for such large UAV swarms. Specifically, we develop a clustering scheme based on which the UAV-based wireless network is partitioned into different clusters coordinated by corresponding cluster-head UAVs forming a connected graph. While the cluster-head UAVs coordinate the ML model updates as in the conventional FL framework, we propose two intercluster model aggregation strategies to produce the final global model in each training round. Extensive numerical studies demonstrate the convergence and desirable trade-offs between training performance and communication efficiency of our proposed framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".