## 9 Discussion
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**No.** & **Defense Technique** & **Advantages** & **Disadvantages** \\ \hline
1 & Detect and Remove & Efficient detection of malicious updates using anomaly detection; prevents poisoned updates from influencing the global model. & Relics on statistical methods, which may struggle with sophisticated attacks. \\ \hline
2 & Adversarial Training & Enhances model robustness by training on adversarial examples; effective for defending against data and model poisoning. & Performance degradation in non-IID data distributions, especially in federated setups. \\ \hline
3 & Model Pruning & Improves computational efficiency and reduces model size, making it harder for attackers to insert malicious updates. & Potential loss in accuracy if essential parameters are pruned. \\ \hline
4 & Byzantine Robust Aggregation & Filtens out malicious updates by identifying outliers and focusing on majority consensus. & Computationally intensive, slowing down convergence in large-scale FL systems. \\ \hline
5 & Robust Client Selection & Enhances the integrity of the aggregation process by selecting reliable clients, reducing the impact of malicious ones. & May exclude valuable data from legitimate but infrequent clients. \\ \hline
6 & Data and Update Analysis & Provides comprehensive defense by analyzing both client updates and local data for consistency. & Requires significant computational resources, slowing the training process. \\ \hline
7 & Homomorphic Encryption & Enables computations on encrypted data, protecting sensitive information during aggregation. & Computationally expensive and slower; challenging for resource-constrained clients. \\ \hline
8 & Knowledge Distillation & Reduces the computational load on edge devices while maintaining performance by transferring knowledge from larger models. & Can introduce information leakage; vulnerable to inference attacks. \\ \hline
9 & Secure Multi-Party Computation & Enables secure collaborative computation without revealing individual data, ensuring high privacy. & Complex to implement and resource-intensive. \\ \hline
10 & Blockchain Integration & Adds transparency and immutability to FL, preventing tampering and providing auditability. & High overhead in terms of computation and storage; not scalable for large networks. \\ \hline
11 & Incentivized Federated Learning & Encourages active and honest participation by rewarding clients based on contributions. & Requires careful incentive design to avoid gaming. \\ \hline
12 & Regularization & Presents overfitting and improves generalization, especially in environments with noisy data. & Over-regularization can reduce model accuracy. \\ \hline
13 & Split Learning & Enhances privacy by keeping sensitive data on client devices and only sharing intermediate representations. & Requires sophisticated coordination, which can introduce delays. \\ \hline
14 & Perturbing Gradients & Protects against gradient-based attacks by adding noise to gradients; ensures differential privacy. & To much noise can degrade model performance, requiring careful tuning. \\ \hline
15 & Differential Privacy & Provides strong privacy guarantees by adding noise to model updates, protecting individual contributions. & Trade-off between privacy and model utility; too much noise can reduce accuracy. \\ \hline
16 & Trusted Execution Environments (TEEs) & Ensursing integrity and privacy by isolating computation inside secure hardware enclaves. & Limited memory and high overhead restrict scalability for large models. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Advantages and disadvantages of defense techniques in Federated Learning.