Quantum Federated Learning for Vehicular Computing Scenarios

With the rise of autonomous vehicles (AVs), the number of self-driving vehicles has increased in recent years and vehicular computing (VC) was actively used to ensure road safety by controlling AVs. However, the recent escalation in demand for a massive scale vehicle network has prevented VC from achieving its purpose. To overcome this problem, a framework which combined VC and federated learning (FL) was proposed. Although the vehicular FL was initially effective, it also faced challenges as the excessive increase in the amount and size of data generated by AVs and the serious threat of data leakage have severely degraded the performance of vehicular FL. Thus, quantum computing was exploited to further improve on the pre-existing FL and proposed dynamic quantum federated learning (DQFL). This work proposes the application of DQFL to public safety scenarios to investigate the effectiveness of the model in controlling the AVs under real-life constraints.