Computational Offloading and Delay Minimization for UAV-aided Edge Federated Learning
Bibliographic record
Abstract
The Internet of things (IoT) applications are generating large volumes of data, and processing this data securely, reliably, and timely is required for effective decision-making. However, the limited processing capability of IoT devices is a significant bottleneck in processing these datasets. A potential solution to overcome this challenge is federated learning using unmanned aerial vehicle (UAV) as mobile edge computing (MEC) servers. In this paper, we propose a UAV-aided edge federated learning (UAFL) framework where we utilize UAV-MEC's computation capacity to process some portion of the datasets from the straggling devices (devices which are unable to process their dataset in reasonable time and are lagging, increasing delay in the whole system). We formulate an optimization problem to minimize system delay considering UAV-MEC's computation power, computation and communication power of IoT devices, and quality of service constraints. We transform the proposed problem by introducing auxiliary variables and epigraph form and then solve the problem using concurrent deterministic simplex with root relaxation algorithm. Simulation results show that UAFL outperforms the traditional federated learning and edge-based learning system by approximately 5%.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".