Semi-Distributed Resource Management in UAV-Aided MEC Systems: A Multi-Agent Federated Reinforcement Learning Approach
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Bibliographic record
Abstract
Recently, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) has been introduced as a promising edge paradigm for the future space-aerial-terrestrial integrated communications. Due to the high maneuverability of UAVs, such a flexible paradigm can improve the communication and computation performance for multiple user equipments (UEs). In this paper, we consider the sum power minimization problem by jointly optimizing resource allocation, user association, and power control in an MEC system with multiple UAVs. Since the problem is nonconvex, we propose a centralized multi-agent reinforcement learning (MARL) algorithm to solve it. However, the centralized method ignores essential issues like distributed framework and privacy concern. We then propose a multi-agent federated reinforcement learning (MAFRL) algorithm in a semi-distributed framework. Meanwhile, we introduce the Gaussian differentials to protect the privacy of all UEs. Simulation results show that the semi-distributed MAFRL algorithm achieves close performances to the centralized MARL algorithm and significantly outperform the benchmark schemes. Moreover, the semi-distributed MAFRL algorithm costs 23<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> lower opeartion time than the centralized algorithm.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it