###### Abstract
Raft is a crash fault tolerance (CFT) consensus algorithm that achieves consensus by electing a leader node and processing transactions sequentially through the leader. It is known for its simplicity, high transaction throughput, and low latency. By leveraging Raft, the proposed system can perform efficient and stable consensus processes, even with a large number of transactions. Moreover, the implementation of Raft in this framework includes an optimization technique that merges and compresses multiple transactions into a single block, further reducing the storage and network overhead associated with the consensus process. This optimization is achieved through a combination of data deduplication and delta encoding, which eliminates redundant data and stores only the differences between transactions. By minimizing the amount of data that need to be stored and transmitted, this technique significantly lowers transaction costs and improves the overall performance of the system. This approach seeks to balance the conflicting demands of data security, privacy, and consensus efficiency. Moreover, to effectively utilize collected EV data for power grid operations, it is crucial to be able to predict energy usage. Especially as renewable energy-based generation expands and bidirectional demand response (DR) becomes more active, it is imperative to enhance demand forecasting capabilities that consider not only individual EV charging/discharging characteristics but also their aggregated impact [16]. Furthermore, there is a need to extend the scope of predictions beyond individual consumers to encompass broader distribution networks such as microgrids. However, this requires the integrated use of data from multiple stakeholders, which is challenging due to privacy issues. A promising alternative is the federated learning (FL) technique [17], which allows multiple clients with distributed data to collaboratively learn a model while keeping the original data private and selectively sharing model parameters. Vertical FL, in particular, enables data owners with different feature spaces to combine features for common samples to train models, effectively facilitating information exchange between electric vehicles and charging stations or aggregating demand data [18]. [PERSON] et al. [19] demonstrated a case where multiple microgrid operators shared demand and generation data under privacy protection through FL, significantly improving prediction performance. Additionally, [PERSON] et al. [20] used an FL model to predict the charging demand for plug-in hybrid electric vehicles (PHEVs) on a cluster basis, reducing peak loads by 23%. Thus, FL emerges as an advanced technology option that captures both privacy protection and enhanced prediction capabilities. However, in the EV environment, where participating vehicles are randomly selected and their data distributions vary, applying basic federated learning algorithms alone may not yield robust prediction models. For instance, methods like FedAvg, which simply averages all local models, are vulnerable to outliers and imbalance issues. According to experiments by [PERSON] et al. [21], if the proportion of malicious clients exceeds 20%, the test accuracy of FedAvg can drop by more than 40%, potentially compromising the reliability of predictions and, consequently, the efficiency of EV and power grid operations. In light of these issues, this study proposes the Federated Learningwith Qualified Local Model Selection (FL-QLMS) technique as part of a Byzantine-robust FL framework. FL-QLMS uses trusted validation data to pre-assess the performance of individual vehicle models and selectively incorporates local models into the global model update based on the results. The trusted validation dataset used in FL-QLMS is selected by a consortium of stakeholders, including EV manufacturers, charging infrastructure operators, energy providers, and government agencies.