UAV Aided Integrated Sensing, Communication and Computing: Optimization via Federated Learning
Bibliographic record
Abstract
Integrated sensing, communication, and computing (ISCC) has emerged as a critical trend in the evolution of 6G networks. However, significant challenges remain in achieving efficient real time sensing and low communication overhead within complex Internet of Vehicles (IoV) environments. This paper innovatively constructs a UAV-assisted three-layer federated learning framework for IoV, which addresses these challenges through the synergistic integration of the distributed characteristics of federated learning with the flexible deployment capabilities of UAVs, thereby enabling efficient collaborative optimization of ISCC resources. First, an enhanced extended Kalman filtering algorithm is designed to acquire real time vehicle state information, providing precise spatiotemporal state inputs for resource allocation. Then, a hybrid decision-making mechanism anchored in proximal policy optimization algorithm is developed to optimize system resource distribution dynamically. Extensive experiments validate the efficacy of the proposed solution, demonstrating that compared with baseline approaches, the methodology achieves performance enhancements of approximately 40.19%, 41.07%, and 51.37% when handling complex tasks involving large-scale data processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".