Position-Aware Structural Knowledge Sharing-Based Federated Graph Learning for Intelligent Transportation Systems
Bibliographic record
Abstract
Federated Graph Learning (FGL) combines the powerful graph data modeling capabilities of Graph Neural Networks (GNNs) with the distributed processing requirements of intelligent transportation systems (ITS), making it a prominent focus in recent research. In the context of the Internet of Everything (IoE), diverse devices and services generate highly heterogeneous, non-independent, and non-identically distributed (non-IID) data, which limits model generalization and training efficiency. To tackle these challenges, this paper proposes a Position-Aware Structural Sharing Federated Graph Learning Framework tailored for ITS applications. This framework enhances GNNs’ capacity to process cross-domain graph data, significantly improving model applicability and performance across various ITS scenarios. Specifically, we use a structural encoder alongside a position-aware structural encoder to capture generic structural knowledge, sharing these embeddings across clients in an FGL setup. Extensive experiments on different datasets demonstrate that our method outperforms existing approaches in handling non-IID federated graph learning tasks, particularly within cross-dataset and cross-domain environments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".