Decentralized Federated Learning over Satellite Networks (Dec-FLSat): A Learning Scheme Based on LEO-Structure
Bibliographic record
Abstract
Federated learning is a promising approach to training deep learning models over distributed devices without sharing their local datasets. Low Earth orbit (LEO) satellites have the potential to use the massive amount of collected Earth imageries and sensor data to train artificial intelligence (AI) models and provide global services such as disaster detection. However, the structure of LEO networks is different from that of the terrestrial networks, which makes the traditional (star-based or hierarchical-based FL) inefficient. This paper proposes a new distributed FL approach that is customized to the LEO structure. The approach is based on parallelizing the FL operations and decentralizing the aggregations over several satellites, aiming at reducing the convergence time at a given energy constraint. Simulation results show that the proposed algorithm converges significantly faster than traditional FL-LEO approaches proposed in the literature under the same energy consumption.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".