# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Simulation Parameters and Setup  
Our methodology's robustness was validated using multiple state-of-the-art datasets, specifically chosen to eliminate potential dataset-specific biases in our evaluation. We intentionally selected datasets with varying levels of complexity to ensure comprehensive testing of our defense mechanism. The MNIST dataset, while widely used, presents relatively straightforward classification challenges that could potentially emphasize the effectiveness of defense methods if used in isolation. In contrast, the CIFAR-10 dataset introduces substantially more complex classification challenges, requiring sophisticated architectures like ResNet18 for effective learning. The consistent superior performance of our method across these diverse datasets substantiates its robust and unbiased nature. To ensure statistical reliability of our results, all experiments were repeated 10 times with different random seeds controlling malicious participant selection and data distribution. All accuracy values reported from Tables 3-13 represent the mean values across these runs. Standard deviations ranged from \(\pm\)0.3% to \(\pm\)1.8%, with higher variance observed in scenarios with greater malicious participation rates. For clarity in table presentation, we report the mean values only. The experimental results revealed an anticipated pattern in classification accuracy, with CIFAR-10 yielding lower accuracy compared to both the MNIST and F-MNIST datasets. This performance differential can be attributed to CIFAR-10's inherent structural complexity, particularly its inclusion of visually similar images across different classification categories. A notable example of this complexity is the visual similarities between Class 3 (cats) and Class 5 (dogs) create natural classification challenges due to shared physical characteristics such as a four-legged structure, ear morphology, and tail presence. In comparison, the MNIST and F-MNIST datasets present more distinctly differentiated image classes, facilitating clearer classification boundaries. Detailed specifications of all datasets employed in this study are presented in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **MNIST** & **F-MNIST** & **CIFAR-10** & **GTSR** \\ \hline Model architecture & CNN & CNN & ResNet18 & CNN \\ Number of parameters & 22 K & 38 K & 11 M & 38 K \\ Toral number of classes & 10 & 10 & 9 & 10 \\ Number of test data & 10,000 & 10,000 & 10,000 & 10,000 \\ Number of training data & 50,000 & 60,000 & 50,000 & 40,000 \\ Number of epochs & 10 & 10 & 10 & 10 \\ Batch size & 32 & 25 & 100 & 32 \\ Image feature & Gray & Gray & Colored & Colored \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model architecture and dataset specifications according to datasets. The simulation environment was designed to test federated learning under diverse participation scenarios and data distributions. For experiments involving the MNIST and F-MNIST datasets, we simulated a network of 100 participating nodes, while the more computationally intensive CIFAR-10 and GTSR datasets were evaluated using a smaller network of 20 participants. To comprehensively assess our system's robustness, we implemented two distinct data distribution strategies: a uniform distribution where data were evenly allocated across participants, and a heterogeneous (non-IID) distribution that better reflects real-world scenarios where data distribution may be unbalanced. To simulate security threats, we randomly designated a varying proportion of participants as adversarial, with the percentage ranging from 0% to 40% of total participants. Each training iteration consisted of a single epoch of local training at each participant node, followed by a security phase where the central server employed detection mechanisms to identify and exclude malicious updates before aggregating the remaining contributions.