Federated Learning for Cellular-connected UAVs: Radio Mapping and Path\n Planning
Bibliographic record
Abstract
To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need\nto fulfill their missions in the shortest possible time. In addition to this\nrequirement, in many applications, the UAVs require a reliable internet\nconnection during their flights. In this paper, we minimize the travel time of\nthe UAVs, ensuring that a probabilistic connectivity constraint is satisfied.\nTo solve this problem, we need a global model of the outage probability in the\nenvironment. Since the UAVs have different missions and fly over different\nareas, their collected data carry local information on the network's\nconnectivity. As a result, the UAVs can not rely on their own experiences to\nbuild the global model. This issue affects the path planning of the UAVs. To\naddress this concern, we utilize a two-step approach. In the first step, by\nusing Federated Learning (FL), the UAVs collaboratively build a global model of\nthe outage probability in the environment. In the second step, by using the\nglobal model obtained in the first step and rapidly-exploring random trees\n(RRTs), we propose an algorithm to optimize UAVs' paths. Simulation results\nshow the effectiveness of this two-step approach for UAV networks.\n
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".