  Optimization-based regularization methods have been effective in addressing
the challenges posed by data heterogeneity in medical federated learning,
particularly in improving the performance of underrepresented clients. However,
these methods often lead to lower overall model accuracy and slower convergence
rates. In this paper, we demonstrate that using Vision Transformers can
substantially improve the performance of underrepresented clients without a
significant trade-off in overall accuracy. This improvement is attributed to
the Vision transformer's ability to capture long-range dependencies within the
input data.
