PSFL: Personalized Split Federated Learning Framework for Distributed Model Training in Intelligent Transportation Systems
Bibliographic record
Abstract
Interest in Intelligent Transportation Systems (ITS) has increased significantly with the development of 6G. Owning an extremely high transmission speed, 6G is able to support low-latency service for edge-intelligence applications by Machine Learning(ML) techniques. However, traditional centralized learning is not suitable for this scenario due to the requirement for users to upload local data to the server, which can compromise data privacy. To overcome this challenge, Federated Learning (FL) and Split Learning (SL), as progressive distributed learning techniques, have been proposed as a solution. They enable the training of ML models while preserving data privacy. However, conventional FL has poor convergence when data heterogeneity occurs, also fails to meet personalized demands. To address these issues, We propose a novel personalized Federated Learning(pFL) framework, which trains models in SL and collaborates in FL. It offers a personalized solution for each client while retaining a global solution for newcomers. Experimental results demonstrate that our method outperforms other advanced baselines on benchmark datasets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 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; a candidate call from one teacher head, 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".