FedStar Caching: Decision Center Assisted Federated Cooperative Edge Caching
Bibliographic record
Abstract
With the explosion of data, Wireless Edge Caching (WEC) has become a promising approach for locally accessing cached contents. Due to the limited storage capacity of local caching devices and the varying preferences of humans for content, it is necessary to predict popular contents. In this article, we will address two distinct objectives: 1) Popularity prediction of the content that should be cached at the edge to effectively utilize edge device memory. 2) Edge devices collaboration for efficient content delivery. Accordingly, we propose an FL-based Star Cooperative Caching (FedStar Caching), utilizing a Star network topology for realizing an efficient cooperation among Femto-cell Access Points (FAPs) using a Decision Center (DC) to enhance power efficiency and delay performance. Considering human sensitivity to privacy, collecting users’ data on a central server is not desirable, therefore, we leverage Federated Learning (FL) to alleviate this challenge. Simulation results demonstrate that the proposed method outperforms alternatives in terms of cache efficiency, as well as delay and power consumption.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".