A Spatio-temporal Split Learning Framework for 5G and B5G Traffic Prediction
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".