Confidence-based federated distillation for vision-based lane-centering
Yitao Chen Dawei Chen Haoxin Wang Kyungtae Han Ming Zhao
A fundamental challenge of autonomous driving is maintaining the vehicle in
the center of the lane by adjusting the steering angle. Recent advances
leverage deep neural networks to predict steering decisions directly from
images captured by the car cameras. Machine learning-based steering angle
prediction needs to consider the vehicle's limitation in uploading large
amounts of potentially private data for model training. Federated learning can
address these constraints by enabling multiple vehicles to collaboratively
train a global model without sharing their private data, but it is difficult to
achieve good accuracy as the data distribution is often non-i.i.d. across the
vehicles. This paper presents a new confidence-based federated distillation
method to improve the performance of federated learning for steering angle
prediction. Specifically, it proposes the novel use of entropy to determine the
predictive confidence of each local model, and then selects the most confident
local model as the teacher to guide the learning of the global model. A
comprehensive evaluation of vision-based lane centering shows that the proposed
approach can outperform FedAvg and FedDF by 11.3% and 9%, respectively.