## 2 Background
### Model Management
The utilization of SMPC-based aggregation plays a crucial role in the FL context, as it enables participants to collectively improve the accuracy of a global model while simultaneously ensuring the protection of their data's privacy. FLSs can utilize safe multiparty computation techniques to use the combined information from various remote sources effectively. This approach ensures that security, privacy, and integrity are maintained during the collaborative learning process. * User credentials authentication: In this mechanism, participants must provide their usernames and passwords to verify their identities. This fundamental mechanism is used extensively despite the fact that, if not adequately secured, it is susceptible to password-based attacks. - Biometric authentication: The authenticity of the participants is determined by using distinctive biological characteristics, such as fingerprints or facial recognition. Because copying biometric data is so complex, this technique provides a very high level of security. - Token-based authentication: Participants authenticate subsequent requests with tokens, which are typically generated once an initial login has been completed successfully. It improves security by minimizing the amount of sensitive credentials that must be transmitted regularly. - Certificate-based authentication: Participants show digital certificates signed by a reliable certificate authority to authenticate themselves. This improves security by guaranteeing that a reliable third party confirms participants' identities. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Local Differential Privacy** & **Central Differential Privacy** & **Epsilon-Differential Privacy** \\ \hline \multirow{3}{*}{Privacy level} & Ensures robust individual privacy by ensuring raw data & Provides privacy at an aggregate level, ensuring that individual data is not directly exposed in any circumstance. & Adjusting epsilon allows for custom privacy levels, offering flexibility. \\ \hline \multirow{3}{*}{Aggregation complexity} & Aggregating locally perturbed data while maintaining privacy is complex. & Since noise addition happens centrally, aggregation is simpler. & The aggregation complexity is determined by the particular implementation and the noise-adding mechanism. \\ \hline \multirow{3}{*}{Centralization} & Completely decentralized, with no centralized entity participating in the processing of data. & The addition of noise necessitates the use of a reliable centralized server, resulting in centralization. & The centralization level depends on the noise-adding mechanism. \\ \hline \multirow{3}{*}{Flexibility} & High privacy but increased noise levels may reduce usability. & A balanced approach with group-level privacy. & Flexible, enabling privacy levels to be changed in accordance with application needs. \\ \hline \multirow{3}{*}{Challenges} & Aggregation complexity. & Central trust. & \\  & Increased noise. & Potential central attack. & Utility trade-off. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison summary between three common differential privacy techniques. Multi-factor authentication: In order to gain access, participants are required to furnish a variety of authentication methods, including a password and a verification number transmitted to their mobile device. The implementation of various proofs of identification enhances the level of security. * oAuth and OpenID connect: The use of secure authentication and authorization protocols is prevalent in web-based FLSs. The system offers standardized and secure authentication techniques, effectively integrating them with a wide range of applications and services. * Device-based authentication: The authentication of participants' devices is contingent upon the utilization of distinct device identifiers or certificates linked to the hardware. Implementing device authorization in the FLS bolsters security measures by only allowing access to authorized devices.