# 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
The framework formulates resource sharing as a combinatorial optimization problem, considering resource consumption and learning quality. It designs a deep reinforcement learning-based algorithm to find the optimal solution. Experimental results demonstrate high accuracy, good convergence, and improved system security. **Committee Consensus Algorithm**. Research employs committee-based consensus algorithms for partially decentralized federated learning, where selected committees reach consensus to decide candidate blocks, as in [129; 130; 131]. In [129; 130], to resist Byzantine attacks and maintain both accuracy and security, committee-based consensus algorithms propose a new federated learning framework. In [131], to prevent Byzantine committee members from compromising the correctness of the global model by tampering with aggregation results, a federated learning scheme called VDFChain is proposed. VDFChain uses lossless masking techniques and trusted committee mechanisms to provide secure aggregation effectively. Security analysis and experimental results demonstrate that the scheme is secure and exhibits excellent computational and communication performance. **PBFT**. PBFT is used in [132; 133; 134] to defend against Byzantine attacks. PBFT consensus is final and deterministic, without chain forks or rollbacks, which guarantees the trustworthiness of federated learning model updates and data integrity. Additionally, PBFT does not rely on high computational power or resources. When applied with blockchain in federated learning, the PBFT consensus algorithm verifies the correctness of global model updates while achieving high efficiency and low energy consumption. In [134], [PERSON]'s secret sharing is combined with blockchain to enhance poisoning robustness and data privacy, leveraging techniques such as quantization, median aggregation, and Hamming distance. Experiments are conducted on the MNIST and CIFAR-10 datasets using a setup with 100 clients and 7 aggregators. Compared to the baseline scheme, the proposed method reduces communication cost by a factor of 76.26 and improves processing speed by a factor of 54.16. Moreover, it maintains a high level of accuracy even when up to 40% of the clients are malicious. In blockchain-based secure aggregation for federated learning, the use of the PoW consensus algorithm incurs extremely high computational costs and energy consumption, making it unsuitable for frequently updated federated learning tasks. However, PoW provides strong security, is tamper-resistant, and is often used as a baseline reference in related studies. In terms of accuracy, [125], which uses PoW, achieves approximately 85%, close to the FedAvg approximation; Ref. [126], employing PoS, reaches about 92%; and [131], using the committee consensus algorithm, achieves 90%. The PoS or DPoS consensus algorithms offer lower computational and energy costs compared to PoW, making them more suitable for medium-frequency federated learning tasks. However, their security relies on the distribution of stake, and they generally provide lower decentralization. For example, Ref. [126] reduces the communication cost per upload by 60%, whereas [125] increases it by 10-20%. Committee consensus algorithms provide fast consensus, high throughput, and low energy consumption. However, the committee or leader election mechanisms may introduce risks of centralization. The computational overhead of [131], which uses the committee consensus algorithm, is roughly the same as that of [126], which uses PoS, with both increasing by about 20%. In high-poisoning environments [133], which uses the PBFT consensus algorithm, achieves an accuracy of up to 80%. Its communication cost increases by approximately 15%, and its computational overhead increases by about 25%. The overall computational cost and energy consumption lie between those of PoW and PoS. Nevertheless, PBFT-based approaches suffer from poor scalability and are more suitable for small-scale scenarios that require high consistency in model updates.