Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Object Detection in Autonomous Driving

PrePrints pp. 1-15,

DOI Bookmark: 10.1109/TMC.2026.3674333

Authors

Yanan Ma, Hong Kong JC Lab of Smart City and the Department of Computer Science, City University of Hong Kong, Hong Kong, China

Senkang Hu, Hong Kong JC Lab of Smart City and the Department of Computer Science, City University of Hong Kong, Hong Kong, China

Zhengru Fang, Hong Kong JC Lab of Smart City and the Department of Computer Science, City University of Hong Kong, Hong Kong, China

Yun Ji, Key Laboratory of Advanced Sensor and Integrated System, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

Yiqin Deng, Hong Kong JC Lab of Smart City and the Department of Computer Science, City University of Hong Kong, Hong Kong, China

Yuguang Fang, Hong Kong JC Lab of Smart City and the Department of Computer Science, City University of Hong Kong, Hong Kong, China

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Keywords

Training, Adaptation Models, Trajectory, Data Models, Object Detection, Convergence, Computational Modeling, Autonomous Vehicles, Optimization, Data Collection, Federated Learning, Autonomous Driving, Crowdsensing, Vehicle Selection, Object Detection, Autonomous Driving, Federated Learning, Vehicular Crowdsensing, Benchmark, Training Data, Data Distribution, Selection Strategy, Fundamental Problem, Training Loss, Visual Model, Trained Data Collectors, Earth Movers Distance, Federated Learning Framework, Optimization Problem, Objective Function, Global Model, Pedestrian, Weighting Factor, Class I, Object Detection Model, Artificial Intelligence Training, Local Updates, Edge Server, Selection Time, IEEE Transactions, Citation Information, Branch And Bound, Similar Technologies, Training Round

Abstract

To accommodate constantly changing road conditions, real-time vision model training is essential for autonomous driving (AD). Federated learning (FL) serves as a promising paradigm to enable autonomous vehicles to train models collaboratively with their onboard computing resources. However, existing vehicle selection schemes for FL all assume predetermined and location-independent vehicles' datasets, neglecting the fact that vehicles collect training data along their routes, thereby resulting in suboptimal vehicle selection. In this paper, we focus on the fundamental perception problem and propose Sense4FL, a vehicular crowdsensing-enhanced FL framework featuring trajectory-dependent vehicular training data collection to improve the object detection quality in AD for a region. To this end, we first derive the convergence bound of FL by considering the impact of both vehicles' uncertain trajectories and uploading probabilities, from which we discover that minimizing the training loss is equivalent to minimizing a weighted sum of local and global earth mover's distance (EMD) between vehicles' collected data distribution and global data distribution. Based on this observation, we formulate the trajectory-dependent vehicle selection and data collection problem for FL in AD. Given that the problem is NP-hard, we develop an efficient algorithm to find the solution with an approximation guarantee. Extensive simulation results have demonstrated the effectiveness of our approach in improving object detection performance compared with existing benchmarks.