Reputation-Aware Scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach
Bibliographic record
Abstract
The rapid integration of unmanned aerial vehicles (UAVs) into the Internet of Things (IoT) has paved the way for the Internet of Drones (IoD). Leveraging advanced technologies, including 5G, IoD introduces new opportunities across various industries. However, with the rapid proliferation of insecure drones, the perspective on IoD has changed from being a facilitator of smart cities to becoming a powerful tool for cyberattacks. To tackle this issue, this paper introduces a novel framework that uses a Multi-Agent federated learning and deep reinforcement learning approach to secure IoD networks against new emerging threats while preserving privacy. Moreover, we develop a reputation-aware scheduling algorithm that allocates bandwidth to reliable drones, emphasizing the reduction of communication expenses during the learning process and prioritizing participants demonstrating superior model learning performance. The effectiveness of our proposed framework is evaluated using real-world IoD-based attack data. The results obtained confirm that our proposed framework improves IoD security while ensuring privacy and resilient defense against potential threats in the IoD ecosystem.
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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 | 0.003 |
| 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) | 0.002 | 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".