# Federated Learning: A Comparative Study of Defenses Against Poisoning Attacks
## 5 Defense Methods
### Krlu
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer Type** & **Size** \\ \hline Conv2D + Stride of 2 + Padding & 64 filters of size 5 \(\times\) 5 \\ Batch Normalization Layer & Normalizes the activations of the previous layer \\ Leaky ReLU Activation Layer & alpha = 0.2 \\ Additional Conv2D + Stride of 2 + Padding & \\ ReLU Activation Layer & Converts the 3D output to a 1D vector for the fully connected layer \\ Flatten Layer & \\ Output Dense Layer & Fully connected layer with 10 neurons \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The DNN architecture used in the CIFAR-10 dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Layer Type** & **Size** \\ \hline Conv2D + ReLU activation & 64 filters of size 5 \(\times\) 5 \\ Additional Conv2D + ReLU activation & 64 filters of size 5 \(\times\) 5 \\ Dropout Layer & Randomly deactivates 25\% of neurons \\ Dense Layer + ReLU activation & 128 fully connected neurons \\ Additional Dropout Layer & Randomly deactivates 50\% of neurons \\ Output Dense Layer & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The DNN architecture used in Fashion-MNIST dataset. #### 6.3.3 The UCI Adult Census Dataset

The UCI Adult Census dataset is a classification dataset that aims to predict whether income exceeds USD 50 K/year based on the 1994 census data. The dataset consists of 45,222 instances, with 14 features (age, work class, education, education-num, marital status, occupation, relationship, race, sex, capital gain, capital loss, final weight, hours per week, and native country), and two classes (>USD 50 K/year or <USD 50 K/year). The training set consists of 30,162 instances and the test set consists of 15,060 instances. The following deep neural network, shown in Table 5, was designed to train the UCI Adult Census dataset. The use of densely connected layers with ReLU activation facilitates effective feature learning, and the dropout layer helps prevent overfitting during the training process. The output layer, with neurons equal to the number of classes (2), is used to generate predictions for the income classification task. #### 6.3.4 The Epileptic Seizure Recognition Dataset

The Epileptic Seizure Recognition dataset consists of 11,500 instances, each with 179 attributes. These attributes correspond to the amplitude of electroencephalogram (EEG) signals recorded at different electrodes and times. The test set consists of 2300 instances and the train set consists of 9200 instances. The dataset is composed of five classes: (1) the epileptic seizure occurred in the EEG, (2) the epileptic seizure did not occur, but the EEG showed abnormal activity, (3) the EEG was recorded outside the brain, (4) the epileptic seizure did not occur, and the EEG was in a normal state and, (5) the epileptic seizure did not occur, but there was abnormal activity in the EEG. The following deep neural network, shown in Table 6, was designed to train the Epileptic Seizure Recognition dataset. The network uses ReLU activation for learning and dropout to prevent overfitting. ## 7 Experimental Results and Discussion

In this section, we present the experimental performance results of the six defense algorithms used. Through a detailed discussion, we explore the strengths and weaknesses of each algorithm, providing valuable insights into their effectiveness in federated learning systems. ### Overall Performance

In this subsection, we present in Table 7 the average results of 10 tests conducted on the Fashion-MNIST dataset, using the default values of the dynamic parameters, as follows: \(\text{IID}=0.4\), number of clients = 100, and percentage of malicious clients = 10%. The KPCA + K-means algorithm achieved the best performance in terms of accuracy, and COOMED achieved the best performance in terms of Precision, Recall, and F1-Score. This can be attributed to the combination of the two techniques Kernel Principal Component Analysis and K-Means. KPCA allows data projection into a higher-dimensional