FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles
Bibliographic record
Abstract
The Internet of Vehicles (IoV) emerges as a pivotal component for autonomous driving and intelligent transportation systems (ITS), by enabling low-latency big data processing in a dense interconnected network that comprises vehicles, infrastructures, pedestrians and the cloud. Autonomous vehicles are heavily reliant on machine learning (ML) and can strongly benefit from the wealth of sensory data generated at the edge, which calls for measures to reconcile model training with preserving the privacy of sensitive user data. Federated learning (FL) stands out as a promising solution to train sophisticated ML models in vehicular networks while protecting the privacy of road users and mitigating communication overhead. This paper examines the federated optimization of the cutting-edge YOLOv7 model to tackle real-time object detection amid data heterogeneity, encompassing unbalancedness, concept drift, and label distribution skews. To this end, we introduce FedPylot, a lightweight MPI-based prototype to simulate federated object detection experiments on high-performance computing (HPC) systems, where we safeguard server-client communications using hybrid encryption. Our study factors in accuracy, communication cost, and inference speed, thereby presenting a balanced approach to the challenges faced by autonomous vehicles. We demonstrate promising results for the applicability of FL in IoV and hope that FedPylot will provide a basis for future research into federated real-time object detection. The source code is available at https://github.com/cyprienquemeneur/fedpylot.
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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 | 0.006 |
| 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 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".