A Spatio-temporal Split Learning Framework for 5G and B5G Traffic Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Accurate traffic prediction is fundamental for enabling energy-aware 5G and Beyond 5G (B5G) cognitive networks. As traffic becomes increasingly heterogeneous and dynamic across geographically distributed sites, existing centralized or fully decentralized machine learning paradigms face significant challenges: centralized models suffer from scalability and privacy concerns, while fully distributed methods often fail to capture global patterns.We propose ST-SplitGNN, a Spatio-Temporal Split Learning Framework for traffic prediction that emphasizes site-level specialization. Each site independently learns the temporal dynamics of its local traffic profile using a dedicated encoder, ensuring that the model adapts to the unique behavior of each site. In addition, nodes transmit intermediate representations to a central server, which aggregates them through a graph neural network (GNN) that models inter-node dependencies.Numerical results show that the proposed approach provides a scalable, communication-efficient and adaptive solution for real-time traffic forecasting, enabling smarter resource allocation and energy-efficient network operations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it