# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Secure Aggregation Algorithms
#### 4.2.4 Secure Aggregation Based on Blockchain  
In terms of security, blockchain is characterized by decentralization, minimizing reliance on a central server. This architecture distributes trust from a single entity to selected aggregators, thereby reducing the risk of a single point of failure. In the context of federated learning, this property is particularly beneficial for secure aggregation, as it prevents any single aggregator from becoming a privacy bottleneck or an attack target. Blockchain's authentication and traceability features ensure that data is both tamper-proof and auditable, which can be directly leveraged to verify the integrity of model updates during the aggregation process. Additionally, the open ledger functionality allows all transactions to be validated by other nodes, thereby enhancing system transparency and providing verifiable guarantees of aggregation correctness. Every model update uploaded to the blockchain must satisfy consensus conditions, and the combination of consensus mechanisms and cryptographic techniques (e.g., digital signatures, zero-knowledge proofs) provides strong resistance against Byzantine attacks, thereby improving the robustness of secure aggregation against malicious participants. Furthermore, interactions between users in a blockchain system are conducted anonymously, with no central server maintaining user information, thereby preserving user privacy during aggregation--a key requirement in federated learning. In terms of efficiency, blockchain can offer incentive mechanisms to ensure that high-performance edge devices are adequately motivated to contribute to the federated learning system. These incentives can also promote honest participation in secure aggregation, encouraging nodes to follow protocols that uphold data privacy. Moreover, participants in the blockchain network can dynamically join or leave while maintaining high availability, which is essential to ensure the continuity of secure aggregation in real-world federated settings. The inherent characteristics of blockchain make its integration with federated learning a promising research direction, and numerous studies have begun to explore this synergy [121; 122]. Unlike traditional federated learning systems, this approach enables fully decentralized federated learning, offering greater flexibility. It can be tailored to diverse scenarios using dynamic aggregation schemes governed by smart contracts, thus enabling secure, automated aggregation without relying on a trusted third party. These smart contracts can enforce privacy-preserving rules and detect anomalies during the aggregation phase, providing an effective solution for handling non-IID data while ensuring security and trust. Figure 11 shows a schematic diagram of secure aggregation based on blockchain. The specific process is as follows: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Scheme** & **Framework** & **Protected Model** & **Network Structure** & \begin{tabular}{c} **Resource** \\ **Requirement** \\ \end{tabular} \\ \hline \multirow{2}{*}{\begin{tabular}{c} Laplace \\ Mechanism \\ \end{tabular} } & [108] & LDP & Local & Decentralization & Lightweight \\  & [109] & LDP & Local and Global & Centralization & Lightweight \\  & [110] & LDP & Local & Centralization & Lightweight \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Gaussian \\ Mechanism \\ \end{tabular} } & [111; 112] & LDP & Local & Centralization & Lightweight \\  & [113] & LDP & Local and Global & Centralization & Lightweight \\  & [114] & GDP & Global & Centralization & Acceptable \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Exponential \\ Mechanism \\ \end{tabular} } & [115] & GDP & Global & Centralization & Lightweight \\  & [116; 117] & LDP & Local & Centralization & Lightweight \\ \hline \multicolumn{1}{c}{Others} & [118; 119; 120] & DDP & Local and Global & Centralization & Acceptable \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Secure aggregation schemes based on differential privacy.