## 3 Results  
### Experimental Design  
For the performance evaluation of the proposed system, the Caltech Adaptive Charging Dataset [16] was utilized, which includes driving and charging logs collected from 100 electric vehicles (EVs) that were operating in California, USA, from January 2018 to June 2019. The dataset comprises logs from various car models such as the Nissan Leaf, BMW i3, and Chevrolet Bolt, with 183 participants (108 males and 75 females) with an average driving experience of 5.2 years. Each log contains 39 fields, including vehicle location (GPS), speed, acceleration, battery level, and energy consumption, totaling 5,124,096 records. The dataset was divided into training (60%), validation (20%), and testing (20%) sets per vehicle. To enhance the realism of the experiment, a non-independent and identically distributed (non-IID) condition was assumed. In real-world scenarios, electric vehicle data are likely to be biased based on factors such as drivers, regions, and driving times, meaning that the distribution of data collected by each electric vehicle may vary. These non-IID conditions can impact the performance of federated learning, and therefore, they were incorporated into the experiment to validate the effectiveness of the proposed technique. The Caltech Adaptive Charging Dataset contains driving and charging log data collected from various electric vehicle models. Due to differences in battery capacity, efficiency, and other characteristics among these models, there may be variations in energy consumption patterns. This suggests that the distribution of data used for training energy consumption prediction models may differ from vehicle to vehicle. Furthermore, data distribution can also vary depending on drivers' driving habits, road and traffic conditions in different regions, and driving time periods. For instance, a group of electric vehicles operating in a specific area is more likely to experience similar road conditions and traffic patterns. As a result, the data distribution of this group may differ from that of electric vehicle groups operating in other regions. Additionally, since each electric vehicle driver has different driving habits, there can be variations in their acceleration and deceleration patterns, the frequency of regenerative braking usage, and more. These factors can influence the distribution of energy consumption data for individual electric vehicles. In each round of federated learning, 10% of all clients were randomly selected to participate, following a uniform random sampling criterion. The local training was set with a batch size of 256 and five epochs using an SGD optimizer (learning rate = 0.01, momentum = 0.9). A learning rate decay (factor = 0.998, step = 100) was applied to minimize performance disparities among clients. The global model employed a three-layer multi-layer perceptron (MLP) with layer sizes of 64, 32, and 16. The ReLU activation function was used, and considering the regression problem, a linear activation was applied in the output layer. The loss function was the mean squared error (MSE), and the performance metrics were RMSE and MAE. The top model selection ratio (k) for FL-QLMS was set at 20%, and all experiments were repeated five times to report average performances. For the Byzantine robustness evaluation, some randomly selected clients were set as Byzantine nodes, simulating scenarios where these nodes send malicious model updates. A global validation dataset for identifying Byzantine nodes consisted of 5000 samples randomly drawn to evenly include data from normal clients. For comparative evaluation of the power consumption prediction model, the same dataset of 100 vehicles was used, split into 80%/20% for training/testing. The same hyperparameter settings described earlier were applied for the multi-stage prediction model, while a single model used an LSTM-based sequence-to-sequence structure [28]. The hidden state sizes for both the encoder and decoder were uniformly set to 64, implemented in a two-layer stacked configuration.