# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Discussions  
Generally, homomorphic encryption and SMPC-based secure aggregation are more resource-intensive but provide robust privacy protection and high model accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Scheme** & **Model Accuracy** & **Protected Model** & **Network Structure** & \
\begin{tabular}{c} **Resource** \\ **Requirement** \\ \end{tabular} \\ \hline \multirow{4}{*}{PoW} & [123] & Increase & Local and Global & Full Decentralization & High cost \\  & [124] & Increase 12.1\% & & \\  & [125] & compared to FedAvg & & \\  & [125] & Increase & Local and Global & Full Decentralization & High cost \\ \hline PoSor DPoS & [126; 127; 128] & Increase & Local and Global & Partial Decentralization & Acceptable \\ \hline
\begin{tabular}{c} Committee \\ Consensus \\ Algorithm \\ \end{tabular} & [131] & Increase & Local and Global & Partial Decentralization & Acceptable \\ \hline PBFT & [132; 133; 134] & Increase & Local and Global & Full Decentralization & Acceptable \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Secure aggregation schemes based on blockchain. Among homomorphic encryption-based secure aggregation. Additive homomorphic encryption is well-suited for federated learning aggregation, offering lower resource requirements compared to fully homomorphic encryption, making it a commonly adopted approach. Fully homomorphic encryption is employed when the entire federated learning workflow requires protection. However, its extensive key generation and encryption/decryption processes often lead to substantial computational and communication overhead. Homomorphic encryption-based secure aggregation is primarily applied in domains with stringent privacy requirements and sensitive data, such as banking risk control and medical diagnosis. In [69], homomorphic encryption is employed to encrypt model parameters for diabetic retinopathy (DR) diagnosis. Experimental results demonstrate that this approach outperforms conventional methods in terms of model accuracy, computational efficiency, and communication overhead. SMPC-based secure aggregation is more computationally efficient than homomorphic encryption but incurs higher communication costs. To address this, most SMPC research integrates lightweight encryption techniques, delivering cost-effective solutions for resource-constrained federated learning environments. SMPC-based secure aggregation is commonly used in scenarios involving numerous participants and end-to-end data security, such as industrial IoT, medical data modeling, and retail enterprises [135; 136]. For example, factory equipment and sensor nodes collect large amounts of data (e.g., temperature, voltage, capacity, etc. ), but different vendors and shops are reluctant to share raw data. Therefore, prediction models can be trained locally on each device, and the model updates are aggregated using SMPC to ensure that specific working condition parameters are not exposed. In federated learning, differential privacy-based secure aggregation is resource-efficient. However, the added noise inevitably degrades model accuracy, making the current research focus on balancing accuracy and privacy protection. This approach is primarily used in resource-constrained and time-sensitive scenarios, such as edge computing on mobile devices, the Internet of Vehicles (IoV), and personalized recommendation systems. If federated learning is applied to the IoV for vehicle trajectory prediction, individual vehicles will generate a large amount of sensitive driving data. To protect privacy while improving the accuracy of path planning, the trajectory data can be perturbed with noise before being uploaded. Blockchain-based secure aggregation leverages blockchain's authentication and traceability features to ensure data integrity and enhance training transparency. Its decentralized nature addresses the single-point-of-failure problem, while consensus mechanisms prevent Byzantine attacks and ensure data validity. Additionally, incentive mechanisms boost the motivation of participants.