## 7 Discussion  
While federated learning (FL) is often recognized as a technique that inherently protects privacy, it can still fall prey to numerous privacy attacks, as discussed in Section 3. The process of exchanging gradient updates across participating nodes in FL might inadvertently lead to potential privacy leaks. These leaks can expose sensitive aspects of the client's private data even without directly sharing the actual training datasets. This vulnerability is amplified due to the large number of participants involved in FL and the transparency of the framework's operations, which could provide ample opportunity for adversaries to launch powerful attacks. In an effort to mitigate these vulnerabilities, our research highlights the potential of two techniques: differential privacy (DP) and homomorphic encryption (HE). In the academic community, DP is often split into three main categories: central differential privacy (CDP), local differential privacy (LDP), and the shuffle model. CDP is designed to shield raw data from potentially malicious clients, thus preventing unauthorized access. However, LDP goes a step further by also protecting data against adversarial servers. This additional layer of security, though, often comes at the expense of model performance due to the added noise. This inherent trade-off gave rise to the exploration of the shuffle model, where privacy is fortified through a process of anonymization and shuffling. This technique severs the link between client-side updates and their origin, adding a further layer of privacy. Despite its advantages, the shuffle model requires trusting the shuffler as an 'honest-but-curious' server, which could be a potential point of vulnerability. Balancing privacy and model performance is one of the major challenges when implementing DP. To ensure privacy, noise is added to the data, which can negatively impact the accuracy of the model. This inevitable trade-off is a critical consideration, prompting our exploration of other potential solutions, such as HE. HE, though computationally expensive, has emerged as a promising technique. It promotes privacy by allowing only aggregated updates to be shared; thus, the aggregation server does not directly observe individual client updates. This approach minimizes accuracy loss, a crucial advantage. Yet, there are still concerns. For example, adversaries might potentially infer useful information from the final model using model inversion attacks. Further, the security provided by HE relies heavily on the strength of the encryption key and the security of the underlying encryption scheme. Unlike DP, it also does not offer plausible deniability, leaving users potentially exposed. As outlined in Section 6, depending solely on one technique leaves potential gaps in security coverage. Therefore, an integrated approach, combining DP and HE, might offer a comprehensive solution. This hybrid model attempts to leverage the strengths of both DP and HE, offering accuracy from HE and plausible deniability from DP. However, this integration is far from straightforward. The challenge lies in navigating the trade-off between privacy, accuracy, and computational complexity to create a robust and efficient privacy-preserving FL framework. In brief, the utilization of DP (differential privacy) and HE (homomorphic encryption) in federated learning can be depicted using Figures 2 and 3. The federated process, utilizing DP and HE, operates through a sequence of two alternating procedures, as depicted in Figures 2 and 3. The sequence kicks off with the server transmitting the global model to the clients. Subsequently, the clients proceed to train a local model and transmit their updates back to the server following the steps illustrated in Figure 2. After that, the server conducts secure aggregation and updates the global model based on the outlined process in Figure 3. These procedures persist until either convergence is reached or the maximum number of iterations is attained. Figure 3: Server process in secure federated learning.