Federated Transfer Learning for Privacy-Preserved Cross-City Traffic Flow Prediction
Bibliographic record
Abstract
Accurate future traffic flow prediction is essential for decision-making in travel recommendations and route planning, aiming to reduce congestion and enhance traffic safety. Traditional traffic flow prediction models often face limitations in quality and structure, leading to increased training costs and inefficiencies, due to data scarcity and centralized training modes that compromise data privacy. To address these issues, we propose a model called 2MGTCN, which combines Multi-modal Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN) for Cross-city Traffic Flow Prediction (TFP). Our 2MGTCN model utilizes federated transfer learning (FTL) to transfer the model from the source to the target domain, mitigating data scarcity. It also incorporates GCN and TCN to capture both spatial and temporal information, enhancing cross-city adaptability. Additionally, Grey Relation Analysis (GRA) and Dynamic Time Warping (DTW) methods are applied to capture road relationships, and a Federated Parameter Aggregation based on Spatial Similarity (FPASS) algorithm is proposed for ensuring effective parameter aggregation by considering spatial similarity. Simulation results show that our 2MGTCN algorithm outperforms traditional TFP models in both centralized and distributed training modes, ensuring higher accuracy and better privacy protection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".