  With the gradual popularization of self-driving, it is becoming increasingly
important for vehicles to smartly make the right driving decisions and
autonomously obey traffic rules by correctly recognizing traffic signs.
However, for machine learning-based traffic sign recognition on the Internet of
Vehicles (IoV), a large amount of traffic sign data from distributed vehicles
is needed to be gathered in a centralized server for model training, which
brings serious privacy leakage risk because of traffic sign data containing
lots of location privacy information. To address this issue, we first exploit
privacy-preserving federated learning to perform collaborative training for
accurate recognition models without sharing raw traffic sign data.
Nevertheless, due to the limited computing and energy resources of most
devices, it is hard for vehicles to continuously undertake complex artificial
intelligence tasks. Therefore, we introduce powerful Spike Neural Networks
(SNNs) into traffic sign recognition for energy-efficient and fast model
training, which is the next generation of neural networks and is practical and
well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme
for SNNs based on neuron receptive fields to extract information from the pixel
and spatial dimensions of traffic signs to achieve high-accuracy training.
Numerical results indicate that the proposed federated SNN outperforms
traditional federated convolutional neural networks in terms of accuracy, noise
immunity, and energy efficiency as well.
