Kati8/ICLR_2023_2025 huggingface dataset:
authors: Enoch Solomon
reviews: 
Comments: This paper introduces an approach to face recognition that leverages federated learning and GANs to address critical privacy concerns. By implementing a decentralized architecture where edge devices independently train models and communicate only processed results to a secure aggregator, the system can ensure sensitive personal data remains protected throughout the entire process.
Strengths: The paper addresses an important topic on face recognition in federated learning.
Weaknesses: 1. The contribution in this paper is not clear. The paragraphs in the Introduction section lacks logic flow about the intuition, challenges and contribution of the work.
2. In Section 3, the proposed method is not sufficiently explained. Figure 1 gives the whole framework, but it lacks comparison with prior research and why this framework is better than prior research. It seems it makes no difference from a traditional FL framework.
3. The experiments do not support the effectiveness of the method. Only one dataset and one model is evaluated.
4. Which GAN model is used in the work? It is not clearly mentioned in the paper.
5. It seems the paper is not a finished work. The writing, presentation of the method, and experiments are way below the ICLR standard.
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: The paper employs a Federated Learning (FL) framework by independently training models on edge devices and sending updates to a central server instead of directly transmitting sensitive raw data. Additionally, it uses Generative Adversarial Networks on edge devices to generate diversified data to enhance privacy protection.
Strengths: To improve model robustness, the paper proposes two methods for generating spoofed image data on edge devices: randomly selecting images of other individuals from the CelebA dataset and using GAN models to generate images.
Weaknesses: 1- Lack of innovation. The proposed method is a combination of several existing methods, and the innovative aspects are insufficient to support acceptance by the conference. 
2- Insufficient experiments. There are too few metrics for measuring facial recognition accuracy, and it is suggested to refer to the literature by Wang et al. (2022). The dataset used is singular, and it is hoped that additional training and testing datasets will be included.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 2
reviews: 
Comments: The paper proposes a privacy-focused face recognition method using federated learning and GANs, where edge devices train models locally and share only processed data with a secure aggregator.
Strengths: The topic is important.
Weaknesses: The paper is overall poorly presented. 
1. The contributions are not clearly stated.
2. The proposed method is just a very simple federated learning process. No innovations at all.
3. Experimental results are very simple and do not provide any insights.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1