Federated Deep Reinforcement Learning for Efficient Jamming Attack Mitigation in O-RAN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Open RAN (ORAN or O-RAN) revolutionizes Radio Access Networks (RAN) by offering flexibility and cost-efficiency through inter-vendor equipment interoperability. More importantly, it addresses emerging security threats, such as jamming attacks, by incorporating network softwarization and leveraging Artificial Intelligence (AI) techniques. However, AI-based systems face challenges such as limited training data, slow convergence, and vulnerability to dynamic attack patterns like Zero-day attacks. To enhance jamming attack mitigation in O-RAN, Multi-Agent Reinforcement Learning (MARL) has been introduced for improved flexibility and robustness. However, MARL requires data sharing, which consumes network bandwidth and slows down training, and the curse of dimensionality limits its benefits due to the exponential growth of the state-action space. To overcome these limitations, we provide a novel framework that combines federated learning (FL) and deep reinforcement learning (DRL) for efficient jamming attack detection in O-RAN. FL allows decentralized agents to train local models using their data sources, and the models are aggregated into a global model at a Non-real-time RAN Intelligent Controller (RIC) to guide decision-making. The federated learning process enables distributed intelligence, while deep reinforcement learning ensures adaptive and robust jamming attack detection. Our proposed framework improves security, privacy, and resilience in ORAN through collaborative FL and adaptive DRL. Extensive simulations demonstrate its superiority in detection accuracy, resource efficiency, and scalability.
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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 | 0.001 | 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