  Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of
death worldwide. Yet, COPD diagnosis heavily relies on spirometric examination
as well as functional airway limitation, which may cause a considerable portion
of COPD patients underdiagnosed especially at the early stage. Recent advance
in deep learning (DL) has shown their promising potential in COPD
identification from CT images. However, with heterogeneous syndromes and
distinct phenotypes, DL models trained with CTs from one data center fail to
generalize on images from another center. Due to privacy regularizations, a
collaboration of distributed CT images into one centralized center is not
feasible. Federated learning (FL) approaches enable us to train with
distributed private data. Yet, routine FL solutions suffer from performance
degradation in the case where COPD CTs are not independent and identically
distributed (Non-IID). To address this issue, we propose a novel personalized
federated learning (PFL) method based on vision transformer (ViT) for
distributed and heterogeneous COPD CTs. To be more specific, we partially
personalize some heads in multiheaded self-attention layers to learn the
personalized attention for local data and retain the other heads shared to
extract the common attention. To the best of our knowledge, this is the first
proposal of a PFL framework specifically for ViT to identify COPD. Our
evaluation of a dataset set curated from six medical centers shows our method
outperforms the PFL approaches for convolutional neural networks.
