  Cross-silo federated learning (FL) has attracted much attention in medical
imaging analysis with deep learning in recent years as it can resolve the
critical issues of insufficient data, data privacy, and training efficiency.
However, there can be a generalization gap between the model trained from FL
and the one from centralized training. This important issue comes from the
non-iid data distribution of the local data in the participating clients and is
well-known as client drift. In this work, we propose a novel training framework
FedSM to avoid the client drift issue and successfully close the generalization
gap compared with the centralized training for medical image segmentation tasks
for the first time. We also propose a novel personalized FL objective
formulation and a new method SoftPull to solve it in our proposed framework
FedSM. We conduct rigorous theoretical analysis to guarantee its convergence
for optimizing the non-convex smooth objective function. Real-world medical
image segmentation experiments using deep FL validate the motivations and
effectiveness of our proposed method.
