A Privacy-Preserving and Edge-Collaborating Architecture for Personalized Mobility
Bibliographic record
Abstract
Driven by technologies and demands, the modern transportation system has developed from intelligent transportation systems (ITS) to autonomous transportation systems (ATS) to resolve intertwined demands and supplies with few human interventions. In ATS, personal mobility service (PMS) is the service that can sense real-time traffic conditions comprehensively, learn travelers’ preferences accurately, recommend multimodal travel options appropriately, and provide service responses timely to elevate the level of personalization and intelligence in smart mobility services. Since current PMS widely employs centralized approaches (CPMS) to process massive sensitive data from individuals and support diverse edge devices, resulting in high pressure in privacy protection and performance balancing, this paper presents a federated PMS (FPMS) and its design architecture in logical and physical views by adopting federated learning to provide multimodal, dynamic, and personalized travel options with system-saving safety and efficiency guaranteed. Moreover, through an extensive evaluation, the performances of CPMS and FPMS are compared to reveal the merits of FPMS in reducing costs and latency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".