# A Review of Research on Secure Aggregation for Federated Learning
## 3 Privacy-Preserving Mechanisms
### Blockchain  
A blockchain is an append-only distributed ledger designed to record transactions among participants, ensuring immutability and resistance to tampering. The blockchain structure is viewed as a linear chain, with transactions appended in batches of blocks. Each block contains transaction data and block header information, and is linked to its immediate predecessor to form the chain. The block header comprises the previous block hash, the Merkle tree root hash, and a timestamp. The structure of the block is illustrated in Figure 7, and the Merkle tree structure is illustrated in Figure 8, using four transactions as examples. Figure 7: The structure of the block. Blockchain provides a unified quantitative benchmark for federated learning, including evaluation metrics for model contribution (accuracy gain), data quality (label accuracy), and training behavior (convergence curve). These standards are enforced through smart contracts, ensuring that all participants collaborate on a fair and transparent basis, thereby enhancing both system credibility and model performance. The consensus mechanism of the blockchain is the core technology that ensures data consistency among network nodes. It enables agreement on the blockchain state through a set of protocols. The consensus algorithm, as the core mathematical method of the mechanism, ensures that all nodes agree on each new block added to the blockchain. The consensus algorithm used in federated learning is as follows:  
* **Proof-of-work (PoW)**: Originally implemented in Bitcoin, the PoW algorithm addresses the issue of consensus through a computational puzzle that is difficult to solve but easy for others to verify. The core idea is to demonstrate validity through computational effort. PoW uses the process of solving puzzles to determine member selection. While PoW enables blockchain membership selection, it demands significant computation, leading to high resource consumption and low transaction throughput [54; 55; 56]. * **Proof-of-stake (PoS)**: The core idea of PoS [57] is that a node demonstrates ownership of virtual resources by staking assets. This ownership determines its eligibility to participate in the consensus process and its level of influence. However, the security of PoS depends on the distribution of assets and places nodes with more assets in a more influential position in system operations. * **Delegated proof-of-stake (DPoS)**: DPoS [58] is a variant of proof-of-stake that introduces a democratic, voting-based membership selection process. Nodes holding stakes vote to select a delegate node, which enables the delegate node to verify and produce blocks. * **Practical Byzantine fault tolerant (PBFT)**: PBFT [59] is a mechanism that remains operational even in the presence of faulty nodes. The PBFT algorithm consists of three primary phases, as follows: the pre-preparation phase, the preparation phase, and the submission phase. The pre-preparation and preparation phases establish a total order of messages, while the preparation and submission phases guarantee consistent request ordering across all nodes. Figure 8: The structure of a Merkle tree is illustrated using four transaction records as examples. The above consensus algorithms are applied to federated learning primarily to enhance decentralization, auditability, participant management, and fault tolerance against malicious clients. The proof-of-work consensus algorithm provides strong tamper-resistance and ensures the integrity of model uploads, offering high security. However, its high latency and computational energy consumption make it unsuitable for real-time federated learning tasks. The proof-of-stake and delegated proof-of-stake algorithms improve training and consensus efficiency compared to proof-of-work, making them more suitable for large-scale federated learning scenarios. However, they offer relatively limited decentralization.