## 4 Discussion and Conclusions
### Limitations and Future Research Directions  
This research is significant academically and practically as it proposes an integrated blockchain-federated learning framework for the utilization of EV data and demonstrates its effectiveness. However, there are several limitations that exist, and this section aims to outline future research directions to overcome these limitations and enhance the completeness of the research. Firstly, large-scale empirical experiments are essential to verifying the actual performance and safety of the proposed system. This research was limited to proof-of-concept simulations; hence, model training and blockchain verification using long-term operational data covering various driving scenarios and network conditions are required [1]. Particularly, ensuring robustness against real data-specific noise factors, such as data quality and collection periodicity inconsistencies and GPS dead zones, is a major challenge. This necessitates the establishment of an automated data preprocessing pipeline, the improvement in data quality through techniques like semi-supervised learning and transfer learning [2; 3], and the development of blockchain stress tests and data integrity verification systems. Secondly, to enhance the social acceptability of the proposed system, building trust among participants and designing fair incentives are crucial. Especially, establishing institutional foundations to dispel privacy concerns associated with data provision and to encourage active participation is urgent [5]. This involves integrating research outputs into regulatory frameworks through standardization and regulatory sandboxes, protecting personal information using anonymized tokens and differential privacy techniques [6], and designing dynamic incentive mechanisms linked to the quantity and quality of data provided [7]. Additionally, policy efforts to improve EV users' perceptions and promote ecosystem participation through technology education and acceptability assessments are required. Thirdly, exploring the potential for broad application across the entire EV value chain is necessary. As the importance of data-driven decision-making is increasing in various areas of the EV industry ecosystem, such as V2G services, battery reuse, and the optimization of charging infrastructure operations [8], establishing data integration and service linkage with these sectors is a key research task. This could involve incorporating V2G participation history and battery performance data into the training dataset or applying multi-agent reinforcement learning [9] for collaborative decision-making across sectors. In the long term, exploring the potential for development into an energy data marketplace encompassing electric, telecommunications, and financial services is also necessary [10]. Fourthly, continuous research to overcome the technical limitations of blockchain and federated learning themselves is required. Considering the constraints of current blockchain platforms in terms of processing performance, scalability, and data confidentiality, performance improvements through integration with cutting-edge cryptographic technologies are necessary. Utilizing zero-knowledge proofs and multi-party computation [11], enhancing privacy-preserving federated learning combined with homomorphic encryption [12], and the strategic application of sharding and off-chain processing are promising approaches. Additionally, exploring lightweight security measures such as selective encryption to balance data integrity and model performance is necessary. Lastly, sophisticated strategies to ensure the Byzantine robustness of federated learning in the long term must be continuously researched. Developing detection and defense mechanisms against cunning adversarial behaviors, such as delay attacks or stealth attacks, is exemplary [13].