Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction

Although AI-empowered schemes bring some sound solutions to stimulate more reasonable energy distribution schemes between charging stations (CSs) and CS providers, frequent data sharing between them is possible to incur many security and privacy breaches. To solve these problems, federated learning (FL) is an ideal solution that only requires CSs to upload local models instead of detailed data. Although the CSs’ electricity consumption need not to be exposed to the server directly, FL-based schemes still have been excavated several security threats such as information exploiting attacks, data poisoning attacks, model poisoning attacks, and free-riding attacks. Hence, in this article, both the effectiveness of energy management and the potential risks of FL for electric vehicle infrastructures (EVIs) are considered, we propose a lightweight authentication FL-based energy demand prediction for EVIs with premium-penalty mechanism. Security analysis and performance evaluation prove that our proposed framework can generate an accurate electricity demand prediction framework to defend multiple FL attacks for EVIs.