However, it was recently shown that gradient inversion attacks can reconstruct this data from the shared gradients.

One concerning capability of these models is their ability to alter small attributes of an original image.

Due to its equivariance property, BITR can not only handle non-overlapped PCs, but also guarantee robustness against initial positions.

We find that it significantly improves the base systems'

Extensive experiments on benchmarks from different DG tasks demonstrate that LFME is consistently beneficial to the baseline and can achieve comparable performance to existing arts.

Prototype Learning (FedPL) addresses this issue, using mean feature vectors as prototypes to enhance model generalization.