# A Review of Research on Secure Aggregation for Federated Learning
## 1 Introduction  
With the explosive growth of data, neural networks have achieved important results in computer vision, speech recognition, agricultural science, and other fields [1, 2, 3, 4, 5]. However, building joint datasets across organizations and individuals faces the dual challenges of data silos and privacy protection. Traditional data processing methods usually adopt centralized collection and unified processing, but they are prone to data leakage, especially in scenarios where multiple users share data [6]. In order to protect user privacy, countries have introduced relevant laws and regulations; for example, the Cyber Security Law of the People's Republic of China clearly stipulates that network operators shall not disclose or provide personal information without authorization. Although these regulations effectively protect privacy, they also make data collection and model training more difficult. In order to protect privacy and solve the data silo problem, federated learning has emerged. In 2016, Google first proposed federated learning [7, 8], the core idea of which is to train models through local devices and send model parameters to a central server for global aggregation, thus realizing localized training of data and privacy protection. Comparedwith traditional centralized machine learning methods, federated learning avoids the risk of privacy leakage caused by centralized data storage and, at the same time, reduces communication overhead and improves computational efficiency. However, federated learning has system vulnerabilities that can be exploited for attacks during the training process to transmit model data. For instance, a malicious centralized server may potentially reconstruct a user's private data, and a malicious participant may also negatively impact the training of the global model. Therefore, designing secure and efficient aggregation algorithms becomes crucial to safeguarding the privacy and performance of federated learning. Current research focuses on combining privacy-preserving mechanisms with aggregation algorithms to address more complex threat models. In addition, since the participants are usually computationally resource-constrained devices, such as mobile terminals, the aggregation schemes also need to be characterized by high communication efficiency and fault tolerance. Given the growing demand for privacy protection, it is crucial to systematically analyze the strengths and limitations of existing security aggregation techniques. In this paper, we categorize and discuss mainstream schemes from the perspectives of security and efficiency, and evaluate their applicability scenarios. The current research review on federated learning mainly focuses on its definition, application, classification, and system security, and seldom focuses on the main aspects of security aggregation. In summarizing the existing literature, we identify the following four deficiencies:  
1. There is no detailed introduction of privacy-preserving mechanisms applied to federated learning, and the discussion of security is limited to describing the development process of integrating privacy-preserving mechanisms with federated learning [9; 10; 11]. 2. In the discussion of security, only the means of attack and the defense methods that can be used are analyzed, and there is no systematic categorization of defense methods based on privacy-preserving mechanisms [12]. 3. The literature only analyzes the possibility of combining individual privacy-preserving mechanisms with federated learning, lacking a side-by-side comparison with other security aggregations [13; 14]. 4. Existing work compares security aggregation algorithms with other aggregation algorithms, focusing on their differences, but lacks a longitudinal comparison of security aggregation algorithms [15; 16].