# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 2 Related Work  
[PERSON] et al. [5] proposed _FedAvg_ as a method for aggregating the learned gradients collected from various systems. It primarily focuses on efficiency through the availability of clients and limited communications. However, it suffers from high computational and communication costs because of client-to-client communication. The major acknowledged work on privacy preservation in FL is the SA proposed in [12]. This approach uses a shared secret key to aggregate client data and, therefore, prevent violations. In the first two rounds, the preparation phase and shared secret keys are generated. In the next round, the devices send cryptographically masked updates, and the server accumulates the sum thereof. All participating device updates are included. In the final round, the model reveals sufficient secret information from the shared secret key to allow the server to unmask the aggregate model. Only devices that have sent their data need to complete this round. This SA is slightly more costly than normal aggregation; however, its costs significantly increase with the number of users, which limits the maximum number of users. SA ensures that the server knows only that one of the users wrote the data without confirming which user wrote them. The main aim of this SA is to prevent users from revealing their identities to the server. The major disadvantages of this method are as follows:  
* The need for client-to-client communication increases the overall cost. * Members participating in masking must create and send everyone's share of the secret information. If the minimum number of secrets required is not shared, unmasking cannot be performed. * If a certain number of members leave the FL process, unmasking is difficult and costly to achieve. The literature survey below offers an introduction to the existing FL algorithms. Most of these are based on client-to-client secret-sharing methods to protect privacy. In contrast, we propose a group-based privacy preservation algorithm that is highly cost-effective. Grouping in FL is not a new process: various methods have been proposed, including stratified/cluster-based sampling groups [17], fork-merge-consolidate [18], the FedEval model [1], and group knowledge transfer [19]. However, most of these methods have categorized and collected data rather than provide security. Hence, we adopt the existing group-based approach and integrate it with the abovementioned SA to offer privacy efficiently. Other methods that offer modified SA and other security algorithms for the FL process are detailed below:  
**FedEval: A benchmark system with a comprehensive evaluation model for FL [1] (2020):** FedEval is an easy-to-evaluate and lightweight federated benchmark system. Its designers customized the traditional message upload methods, server aggregation training method, and global-local model incorporation. The authors described FedSGD and FedAvg as the two most common methods in FL. FedSGD operates via large-batch synchronous stochastic gradient descent. FedAVG operates in small batches; it trains over various rounds and increases the number of clients in each round. Therefore, FedAvg is a more efficient method. However, it is less accurate than FedSGD and requires separate validation steps after uploading the actual updates, which increases the number of communications required. The algorithm protects the data from a gradient attack, using SA as its base; hence, it suffers from efficiency issues, as discussed at the beginning of this section. **Federated learning with differential privacy: Algorithms and performance analysis [11] (2020):** This approach is based on increasing an optimal amount of the noise in the data to design a privacy-preserving FL architecture. The more noise is added, the more privacy is achieved. However, convergence performance is reduced with the increase in noise. Hence, there is always a trade-off between the convergence performance and privacy degree. The training quality is also affected by the size and distribution of the data.