# 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 for applying blockchain technology in federated learning can be classified into two categories:  
* **Full decentralization**: In fully decentralized federated learning, each node can act as a consensus node, playing the role of a central server to lead a training round, with a probability proportional to its available resources. * **Partial decentralization**: In partially decentralized federated learning, the system balances computational cost and security by algorithmically selecting candidate blocks or designating nodes responsible for selecting candidate blocks to achieve an optimal trade-off. Secure aggregation based on blockchain can be categorized according to the consensus algorithms used, with existing approaches divided into the following four categories:  
**PoW**. The PoW consensus algorithm is used to construct federated learning systems in [123; 124; 125]. In [123], to solve the single-point-of-failure problem, a blockchain-assisted decentralized federated learning framework is proposed. In this framework, the model aggregation process is fully decentralized, integrating model training and blockchain mining tasks for each participant. This design effectively prevents malicious clients from poisoning the learning process and provides clients with a self-motivated and reliable learning environment. Experimental results show that when 30% of clients are inert, the scheme improves accuracy by 18% and 13.8% on the Fashion-MNIST and CIFAR-10 datasets, respectively, compared to the baseline. Figure 11: Schematic of secure aggregation based on blockchain. Ref. [124] proposes a blockchain-based asynchronous federated learning scheme to ensure security and efficiency. The scheme introduces a new entropy weight method to evaluate the participation level and proportion of local models trained on devices. Experiments demonstrate that the scheme reduces latency by 66% compared to synchronous federated learning and improves accuracy by 12.1% over FedAvg. In [125], a fully coupled blockchain-assisted federated learning system is proposed to eliminate the single point of failure by decentralizing training and aggregation tasks among all participants. The system offers high flexibility, allowing participants to choose shared models and customize aggregation according to local needs, optimizing system performance and yielding accurate inference results. Experimental results indicate that this scheme can train large and complex models with an accuracy loss of less than 0.5%. **PoS or DPoS**. To improve the communication efficiency, the construction of federated learning systems is performed using PoS or DPoS consensus algorithms, e.g., [126; 127; 128]. In [126], to enhance edge node caching efficiency and communication efficiency in federated learning, an algorithm called CREAT is proposed. This algorithm predicts the popularity of different files to cache them, thereby speeding up responses to IoT device requests. Combined with blockchain technology, it ensures the security of data transmitted by IoT devices and gradients uploaded by edge nodes. Experiments using the MovieLens dataset show that this algorithm significantly optimizes data upload time compared to baseline algorithms. In [127], a blockchain-based hierarchical crowdsourced federated learning system is proposed to assist manufacturers in developing smart home systems. This system uses a reputation mechanism to help appliance manufacturers train models with customer data while protecting extracted features with differential privacy. Experimental results indicate that its communication cost is acceptable. Assuming a local model size of 617.8 KB and an upload bandwidth of 1 MB/s, the communication time is approximately 0.6178 s.  
Ref. [128] considers potential scenarios where federated learning is applied to 5G and beyond. It proposes a framework combining federated learning with blockchain to improve model quality, communication efficiency, and privacy security.