A Two-Dimensional Hybrid Federated Learning Framework for Secure Data Cooperation of Multiple Network Service Providers
Bibliographic record
Abstract
In the era of artificial intelligence of things (AIoT), the substantial user data collected by network service providers (NSPs) holds great potential for enhanced service provisioning. However, the data isolation between NSPs limits the full utilization of data. In this article, we investigate collaborative computing among NSPs to fully unlock the potential of heterogeneous cellular data, with comprehensive discussions on its advantages, applications, and challenges. We propose a two-dimensional hybrid federated learning (2DHFL) framework to facilitate collaborative computing among multiple NSPs, with ingenious integration of horizontal and vertical federated learning techniques. The 2DHFL framework can effectively address the challenges related to incomplete features and insufficient training data while maintaining data privacy. A case study on map matching task is presented with empirical data-driven experiment results to demonstrate the effectiveness of the 2DHFL framework compared with state-of-the-art benchmark schemes.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.030 | 0.041 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".