## 3 Results
### Performance Evaluation of Prediction Models
This selective aggregation process helps to mitigate the impact of outliers or malicious nodes, enhancing the robustness and efficiency of the federated learning process. In this experiment, the FL-based methods used data partitioned by vehicles, and the data distribution for each vehicle was assumed to be not independently and identically distributed (non-IID). Additionally, to simulate malicious participation, 20% of the vehicles were set as Byzantine nodes, which may send incorrect or manipulated updates to disrupt the learning process. The analysis showed that vanilla FL suffered significant performance degradation in non-IID and Byzantine environments. This can be attributed to its simple model averaging strategy, which treats all local updates equally and thus fails to effectively mitigate the influence of outliers or malicious updates. In contrast, partially decentralized FL achieved comparable performance to the centralized approach, even under Byzantine attacks. By selectively aggregating only the top 20% of local models and assigning weights proportional  
Figure 5: Comparison of test accuracy between centralized FL, vanilla FL, and partially decentralized FL methods. to the nodes' data size, partially decentralized FL effectively minimized the impact of low-quality updates. Consequently, it demonstrated around 40% higher test accuracy compared to vanilla FL in non-IID settings. These results not only highlight the accuracy gains of partially decentralized FL but also demonstrate its scalability and robustness advantages, which become increasingly important as the number of participating nodes grows from thousands to tens of thousands. The selective aggregation mechanism of partially decentralized FL acts as a defense against Byzantine attacks, preventing individual malicious nodes from significantly degrading the global model. Moreover, by reducing the number of local models that need to be communicated and aggregated, partially decentralized FL can substantially improve communication efficiency and reduce overall training time. However, it is important to note that implementing partially decentralized FL in real-world EV scenarios still faces challenges related to privacy protection, incentive mechanism design, and the establishment of trusted validation datasets. Nonetheless, the experimental results strongly suggest that partially decentralized FL is a promising approach for enabling reliable and efficient federated learning in the presence of non-IID data distributions and potential Byzantine attacks, making it a valuable tool for harnessing the power of decentralized EV data. In summary, the combination of multi-stage prediction and partially decentralized FL techniques offers a comprehensive solution for enhancing the accuracy, scalability, and robustness of energy consumption forecasting in electric vehicles. By capturing fine-grained spatio-temporal patterns and effectively mitigating the impact of data heterogeneity and Byzantine threats, this approach paves the way for more efficient and reliable energy management strategies at both the individual vehicle and the fleet level. Moreover, the insights gained from this study can potentially inform the development of data-driven optimization frameworks for related applications, such as charging infrastructure planning and grid-load balancing, ultimately contributing to the sustainable integration of EVs into smart energy systems. However, it is important to acknowledge the limitations of the current experiments, such as the relatively small dataset size and the simplified assumptions regarding non-IID distributions and Byzantine behavior. Future research should aim to validate the proposed methods on larger and more diverse datasets, incorporating realistic factors such as seasonal variations, driver-specific characteristics, and complex attack scenarios.