Split Federated Learning-Driven Resource-Efficient MEC Framework for UAV-Based Networks
Bibliographic record
Abstract
Distributed collaborative machine learning techniques enable the training of intelligent models while preserving user data privacy. However, in reality, training a large-scale and intricate model on resource-constrained devices such as Unmanned Aerial Vehicles (UAVs) is unfeasible. In this context, lightweight and resource-efficient deep learning techniques are required. This work first suggests a new resource-aware distributed framework, SFMec, in the context of a UAV power consumption scenario. The framework is evaluated and compared with other distributed frameworks, including FedMec, a federated learning-based approach, to assess its performance across different system architectures and resource management strategies. The results obtained demonstrate that SFMec has the potential to conserve more than 50% of the storage space occupied by FedMec, making it more attractive for devices with limited resources. Then, a novel architecture, denoted as SFMecLite, is introduced to minimize the interactions between SFMec entities. Furthermore, an enhanced version of SFMecLite is also presented that greatly outperforms FedMec and reduces the computational and communication costs in SFMec without compromising learning performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".