# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 2 Related Work
These are the major limitations of the approach, which leads us to think of the alternative of the technique. GS can be a better option because GS does not add any noise to the data, and so it does not affect the convergence performance of the algorithm. **Communication/computation-efficient SA for FL [20] (2021):** This algorithm adopts the same four steps as the conventional scheme of SA: (1) advertising keys, (2) sharing keys, (3) mask input collection, and (4) unmasking. However, the key is shared with a subset of the clients (instead of all clients). This is more efficient than the conventional protocol because the involvement of fewer clients reduces the number of communication messages, and the overall computational cost is reduced using a smaller number of masks. However, this algorithm still requires client-to-client communication, making it less efficient when the network size is increased. **Breaking the quadratic aggregation barrier in secure FL [21] (2021):** This approach uses secure multi-party computation based on cryptographic primitives, including secret sharing. Although the algorithm ensures privacy, it also suffers from high communication burdens when reconstructing secret information distributed over multiple clients. **Learning rate optimization for FL exploiting over-the-air computations [22] (2021):** A dynamic learning rate has been proposed for efficient data aggregation in over-the-air computation. The proponents used parallel computing with multiple-input single-output and multiple-input multiple-outputs using ML to reduce the overall computation time. However, they only considered averaging and focused on minimizing the aggregate error. Overall, client-to-server communication and privacy were not discussed in detail. **Decentralized federated averaging: [23] (2015):** The authors implemented FedAvg in a distributed manner because they assumed that attacking the central server would break the entire system's privacy. They implemented the model on clients that were connected by an undirected graph. To further reduce the communication costs, they considered the quantized decentralized FedAvg with momentum. However, decentralized FL makes the algorithm less secure because the devices must trust one another. **FedV: Privacy-preserving FL over vertically partitioned data [24] (2021):** To avoid existing client-to-client communications in horizontal FL, the authors proposed a vertical FL that used functional encryption to secure their data. This reduced the training time, and no issues arose when clients could not connect. To the best of our knowledge, this study is the first algorithm that protects both client identification and data privacy. The server does not know which update belongs to which client, and at the same time it does not know the previous sequence of update of the current update. Hence, the server can neither predict the data nor the owner of the data, which mitigates the chances of inference attacks. Our algorithm effectively reduces the computational cost and network bandwidth while providing the necessary security features. Section 5 presents a detailed comparison between the proposed algorithm and existing methods.