HCP: Heterogeneous Computing Platform for Federated Learning Based Collaborative Content Caching Towards 6G Networks
Why this work is in the frame
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Bibliographic record
Abstract
A heterogeneous computing architecture is essential to facilitate intelligent network traffic control for a joint computation, communication, and collaborative caching optimization in 6G networks to provide stringent Quality of Experience (QoE) guarantees. In this paper, we consider a 6G integrated aerial-terrestrial network model where Unmanned Aerial Vehicles (UAVs) and terrestrial Remote Radio Heads (RRHs) jointly serve as heterogeneous Base Stations (hgNBs) of a Cloud Radio Access Network (HCRAN) serving different mobile user (UE) types. We propose a distributed heterogeneous computing platform (HCP) across the UAVs and terrestrial Base Stations (BSs) by utilizing their caching and cooperative communication capabilities. In order to preserve the privacy of the content of the UEs, we propose a 2-stage federated learning algorithm among the UEs, UAVs/BSs, and HCP to collaboratively predict the content caching placement by jointly considering traffic distribution, UE mobility and localized content popularity. An asynchronous weight updating method is adopted to avoid redundant learning transfer in the federated learning. Once the global model is learnt by the HCP, it transfers the learned model to the UEs to facilitate the much desired edge intelligence in the considered 6G tiny cell. The effectiveness of the proposal is evaluated by extensive numerical analysis.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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