  How to tackle non-iid data is a crucial topic in federated learning. This
challenging problem not only affects training process, but also harms
performance of clients not participating in training. Existing literature
mainly focuses on either side, yet still lacks a unified solution to handle
these two types (internal and external) of clients in a joint way. In this
work, we propose a unified framework to tackle the non-iid issues for internal
and external clients together. Firstly, we propose to use client-specific batch
normalization in either internal or external clients to alleviate feature
distribution shifts incurred by non-iid data. Then we present theoretical
analysis to demonstrate the benefits of client-specific batch normalization.
Specifically, we show that our approach promotes convergence speed for
federated training and yields lower generalization error bound for external
clients. Furthermore, we use causal reasoning to form a causal view to explain
the advantages of our framework. At last, we conduct extensive experiments on
natural and medical images to evaluate our method, where our method achieves
state-of-the-art performance, faster convergence, and shows good compatibility.
We also performed comprehensive analytical studies on a real-world medical
dataset to demonstrate the effectiveness.
