  The success of machine learning applications often needs a large quantity of
data. Recently, federated learning (FL) is attracting increasing attention due
to the demand for data privacy and security, especially in the medical field.
However, the performance of existing FL approaches often deteriorates when
there exist domain shifts among clients, and few previous works focus on
personalization in healthcare. In this article, we propose FedHealth 2, an
extension of FedHealth \cite{chen2020fedhealth} to tackle domain shifts and get
personalized models for local clients. FedHealth 2 obtains the client
similarities via a pretrained model, and then it averages all weighted models
with preserving local batch normalization. Wearable activity recognition and
COVID-19 auxiliary diagnosis experiments have evaluated that FedHealth 2 can
achieve better accuracy (10%+ improvement for activity recognition) and
personalized healthcare without compromising privacy and security.
