Federated reinforcement learning based task offloading approach for MEC-assisted WBAN-enabled IoMT
Why this work is in the frame
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Bibliographic record
Abstract
The exponential proliferation of wearable medical apparatus and healthcare information within the framework of the Internet of Medical Things (IoMT) introduces supplementary complexities pertaining to the elevated Quality of Service (QoS) of intelligent healthcare in the forthcoming 6G era. Healthcare services and applications need ultra-reliable data transfer and processing with ultra-low latency and energy usage. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies enabled IoMT to handle large amounts of data sensing, transmission, and processing while maintaining good QoS. Traditional frame aggregation (FA) systems in WBAN, on the other hand, create an excessive number of control frames during data transmission, resulting in significant latency and energy consumption, as well as a lack of flexibility. A Federated Reinforcement Learning (FRL) based TO Approach is recommended in this research. In the beginning, different types of service-related information were separated into queues with equal QoS needs. The duration of the FA was then automatically determined by the aggregation vertex based on energy consumption, latency, and throughput using FRL. Finally, based on the existing status, the amount of tasks offloaded was determined. The simulation results demonstrate that, as compared to the baseline schemes, the suggested FRLTO efficiently reduces energy consumption and latency while enhancing throughput and total WBAN utilization. Numerical results show that the proposed scheme improves the throughput by 37.06% and reduced the energy consumption by around 69.84% and time delay by about 6.23%, as compared to the state-of-the-art existing baseline schemes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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