S-EVPF: A Secure and Decentralized EV Prediction Framework using Federated Learning

The S-EVPF framework introduces a secure, decentralized solution for improving prediction models in the electric vehicle (EV) industry, addressing key concerns around privacy and data security. It enables multiple EVs to work together to enhance predictions related to battery health, maintenance schedules, time, and route efficiency, all while keeping sensitive data private. By using federated learning, EVs can contribute to a shared model without needing to centralize their data, benefiting from a wide range of information while complying with privacy rules. What sets S-EVPF apart is its use of blockchain technology to secure the exchange of data and model updates. This creates a permanent, auditable record of transactions and changes among all participants, ensuring the integrity and transparency of the system. By combining federated learning with blockchain, S-EVPF offers a solution that boosts both the accuracy and security of EV predictions. The framework not only enhances data protection but also encourages collaboration across the EV ecosystem, helping improve prediction performance and build trust. S-EVPF holds great promise for strengthening the decentralized management of EVs, making it a scalable and secure option for the industry.