reviews: 
Comments: The authors aim to explore the privacy issues in federated learning. The idea is to employ GANs to diversify data locally, eliminating the need for data transmission and thus preserving privacy throughout the process. The aggregator combines these diverse models into a single global model, which is then distributed back to the edge devices for further enhancement.
Strengths: + This work considers a practical scenario- a face verification system, which is meaningful for exploring privacy issues.
Weaknesses: However, this work has the following major concerns:

**Lack of Novelty and Technical Contribution**: The idea of using GAN to generate impostor images is very straightforward. As explained on Page 4, "In the system incorporating a secure aggregator, the process unfolds with distinct steps, including Local Training, Model Transmission to Secure Aggregator, Global Model Creation, Aggregated Model Transmission, and Distribution to Devices. Conversely, in the absence of a secure aggregator, the workflow proceeds similarly, albeit without involving the aggregator." The core idea is simply using secure aggregation, which is not novel. Recently, a lot of works improve the secure-aggregation-based federated learning for the goal of efficiency or scecurity.  For example,

[Reference 1] FedCSCD-GAN: A secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and GAN.

[Reference 2] Ifl-gan: Improved federated learning generative adversarial network with maximum mean discrepancy model aggregation.

[Reference 3] Scionfl: Efficient and robust secure quantized aggregation

[Reference 4] Cryptography-inspired federated learning for generative adversarial networks and meta-learning.

[Reference 5] Sear: Secure and efficient aggregation for byzantine-robust federated learning

[Reference 6] Efficient Aggregation of Face Embeddings for Decentralized Face Recognition Deployments.

[Reference 7] A comprehensive experimental comparison of the aggregation techniques for face recognition

Moreover, secure aggregation with GAN has been proposed in reference 4, which supports stronger security than this work.

 **Insufficient Experimental Validation**: This work shows experiments conducted on the CelebA dataset. However, it does not provide a sufficient evaluation of other datasets (e.g.,  Wild (LFW) and YouTube Faces (YTF) datasets) or compare their work with other advanced works. The experimental results are very limited. This work lacks sufficient experimental results compared with related works (e.g., reference 3, reference 4, reference 6, and reference 7 as mentioned) that improve secure aggregation or federated GAN.

**Lack of Security Analysis** The security model is not clear and not defined. Accordingly, the formal security analysis also remains lacking. There is no explanation or assumption of the attackers, e.g., the attacker's knowledge, malicious or honest. The explicit privacy guarantee is also not defined. Additionally, secure aggregation is not secure againset some attacks, such as,

[reference 8] Secure Aggregation is Not Private Against Membership Inference Attacks.

**Severe Wring issues.** The introduction does not give a clear summary of motivation and contribution to this work, such as improvement to prior works. For example, the work does not explain why not adopt the better secure aggregation (for example, reference 3) for improved efficiency. The related works of secure aggregation and federated GAN lacks. The explicit motivation to adopt secure aggregation and federated GAN in face recognization remains lacking. Besides, the figure size is weird, e.g, Figure 1 and Figure 3. The writing quality should be hugely improved to meet the paper's qualifications.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1