One potential limitation of the synthetic data generation method in FedImpres is that it may not fully capture the diversity and complexity of the underlying data distributions across different clients. While the current approach focuses on generating high-fidelity images that preserve global information, it may still miss out on specific local features that are crucial for accurate classification, especially in heterogeneous datasets.
To improve this, the synthetic data generation process could incorporate a more dynamic feedback mechanism that continuously learns from the local models' performance. By analyzing the classification errors and the characteristics of the local datasets, the synthesis process can be adjusted to generate samples that specifically address the weaknesses of the local models.
Moreover, integrating techniques such as conditional GANs could allow for the generation of synthetic data that is conditioned on specific classes or features, thereby enhancing the relevance of the generated samples. This would ensure that the synthetic data not only reflects the global distribution but also aligns closely with the local data characteristics.
Another improvement could involve the use of ensemble methods, where multiple generative models are trained and their outputs are combined to create a more comprehensive synthetic dataset. This ensemble approach can help in capturing a wider range of variations and nuances present in the data, leading to better generalization during local training.