The motivation for the federated image translation remains unclear. Authors refer to [1] for the motivation of federated I2I and provide an example of the medical domain, but there are significant issues with both arguments. First, [1] is not accepted to a peer-reviewed venue, which makes the use of any arguments from that paper questionable. Second, it appears that for the majority of medical applications, federated I2I is rather impractical, because a) I2I, in general, requires a large amount of data which is usually not achievable for most medical applications; b) I2I is unreliable as it may not preserve the important information of the input image in the translation result, which is very undesirable for the medical domains. For most of the other applications generally discussed in the I2I literature, the privacy issue is usually not a concern.
The main idea behind this method, which is to trivially split the CUT loss into one that only uses the samples from the source domain (Eq. 12) and the one that uses target domain examples (Eq. 11) provides very limited novelty. In fact, [1] uses a very similar and rather trivial loss decomposition strategy. The use of classifier weights for discriminator initialization is also a very well-known technique in the literature.
Even if we disregard the triviality of the proposed approach, the proposed training strategy is impractical. Even though the proposed method implies roughly 500 times less data transfer than Federated CycleGAN [1] that required transmitting the weights of two generator-discriminator pairs (Table 1), it still requires the transfer of roughly 4.2 MB of data per iteration, which would make training of a single I2I model last for months.
In the experiments section, the authors compare the proposed method with the Federated CycleGAN, CUT and FastCUT, and report the FID scores and segmentation quality on the popular I2I datasets. It appears that the reported improvement in the translation quality comes from either random weight initialization or due to the use of the pretrained classifier weights (because everything else is essentially the same as in FastCUT). Ideally, an average score over several randomly initialized runs must be reported to exclude the first factor, and an ablation study with the randomly initialized discriminator should be held to illustrate the second factor. Also, since the main claim of the paper is the federation component, it is crucial to report the amount of time spent on training of all methods discussed above vs the proposed method.
The related work section misses an overview of the existing non-federated I2I methods.
[1] Park, Taesung, et al. "Contrastive learning for unpaired image-to-image translation." European Conference on Computer Vision. Springer, Cham, 2020.
[2] Song, Joonyoung, and Jong Chul Ye. "Federated CycleGAN for Privacy-Preserving Image-to-Image Translation." arXiv preprint arXiv:2106.09246 (2021).
12) and the one that uses target domain examples (Eq.