# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Secure Aggregation Algorithms
#### 4.2.3 Secure Aggregation Based on Differential Privacy  
Differential privacy protects individual records from inferential attacks by introducing random noise to the model data. Typically, the added noise ensures that the risk of privacy leakage remains minimal and acceptable. Unlike cryptographic approaches, differential privacy-based methods generally incur low computational overhead. However, the introduction of noise during the learning process can lead to a reduction in the accuracy of the trained model. Differential privacy frameworks in federated learning can be classified into three categories, as follows:  
* **Local differential privacy (LDP)**: Local differential privacy is designed for scenarios with untrusted servers, where each client adds perturbation noise to its data before sending it to the central server. By ensuring that the added noise satisfies the client's differential privacy requirements, the client's privacy remains protected regardless of the actions or behaviors of other clients or the server. * **Global differential privacy (GDP)**: Global differential privacy is typically employed in scenarios involving a trusted server, where the server adds differential privacy-compliant noise during the aggregation process. This approach protects user privacy while enabling the construction of a more practical model by introducing controlled noise into the global model maintained on the server. * **Distributed differential privacy (DDP)**: Distributed differential privacy does not require a trusted server and aims to achieve strong privacy guarantees with minimal noise addition. The system must pre-determine the noise budget required for each training round to ensure sufficient privacy protection. In each round, the noise addition task is allocated evenly among clients, each of which adds the minimum necessary noise to perturb its model update. Subsequently, the client's update is masked before transmission to ensure that the server learns only the aggregated result. Distributed differential privacy typically incurs substantial communication overhead. Secure aggregation schemes based on differential privacy can be categorized according to their noise generation mechanisms. Existing studies are typically divided into the following three categories:  
**Laplace mechanism**. Data models for federated learning are protected by differential privacy based on the Laplace mechanism, as presented in [108; 109; 110]. In [108], to prevent attackers from partially reconstructing the original training samples using model parameters, a federated learning scheme that combines model segmentation \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Scheme**} & **Support for User** & \multirow{2}{*}{**Protected Model**} & \multirow{2}{*}{**Network Structure**} & **Resource** \\  & & **Disconnection** & & & **Requirement** \\ \hline \multirow{3}{*}{Shamir} & [89; 90; 91] & Support & Local & Centralization & Lightweight \\  & [92] & Support & Local & Decentralization & High cost \\  & [93; 94] & Support & Local and Global & Centralization & Acceptable \\ \hline \multirow{3}{*}{Verifiable Secret Sharing} & [95] & Support & Local and Global & Trusted party and server & High cost \\  & [96] & Support & Local and Global & Decentralization & High cost \\  & [97; 98] & Support & Local and Global & Centralization & Lightweight \\  & [99] & Not supported & Local and Global & Centralization & Lightweight \\ \hline \multirow{3}{*}{Additive Secret Sharing} & [100] & Not supported & Local & Centralization & Acceptable \\  & [102; 103] & Not supported & Local & Decentralization & Acceptable \\  & [101; 104] & Support & Local & Centralization & Lightweight \\  & [105] & Support & Local and Global & Centralization & Acceptable \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Secure aggregation schemes based on secure multi-party computation. techniques with differential privacy methods is proposed for mobile edge computing.