Federated learning can effectively protect local data privacy in 5G-V2X environment and ensure data protection in Internet of vehicles environment. The advantages of low delay of 5G network should be better utilized in the vehicle-road cooperative system. But the existing asynchronous federated learning obtains a local model through different node training and completes the update of the global model through the central server. There are problems such as single point of failure, privacy leakage, and deviation of aggregation parameters. In response to the above problems, we proposed a 5G-V2X-oriented asynchronous federated learning privacy-preserving computing model (AFLPC). We used an adaptive differential privacy mechanism to reduce noise while protecting data privacy. A weight-based asynchronous federated learning aggregation update method is proposed to reasonably control the proportion of parameters submitted by users with different training speeds in the aggregation parameters and actively update the aggregation parameters of lagging users, so as to effectively reduce the negative impact on the model caused by the different speed of finding you. Experiments show that the proposed method can effectively ensure the credibility and privacy of asynchronous federated learning in 5G-V2X scenarios and at the same time improve the utility of the model.

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With increasing consumption of energy and increasing environmental pollution, research on capturing the vibration energy lost during transportation and vehicle driving is growing rapidly. There is a large amount of vibration energy in the automobile exhaust system that can be recycled. This paper proposes a self-powered intelligent device (SPID) using a piezoelectric energy generator. The SPID includes a piezoelectric generator and sensor unit, and the generator is installed at the end of the automobile exhaust system. The generator adopts a parallel structure of four piezoelectric power generation units, and the sensing unit comprises light-emitting diode warning lights or low-power sensors. A simulated excitation experiment verifies the working state and peak power of the piezoelectric generator unit, which can achieve 23.4 μW peak power. The self-power supply and signal monitoring functions of the intelligent device are verified in experiments conducted for driving light-emitting diode lights and low-power sensors. The device is expected to play a crucial role in the field of intelligent driving and automobile intelligence.

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