  Federated learning facilitates the collaborative learning of a global model
across multiple distributed medical institutions without centralizing data.
Nevertheless, the expensive cost of annotation on local clients remains an
obstacle to effectively utilizing local data. To mitigate this issue, federated
active learning methods suggest leveraging local and global model predictions
to select a relatively small amount of informative local data for annotation.
However, existing methods mainly focus on all local data sampled from the same
domain, making them unreliable in realistic medical scenarios with domain
shifts among different clients. In this paper, we make the first attempt to
assess the informativeness of local data derived from diverse domains and
propose a novel methodology termed Federated Evidential Active Learning (FEAL)
to calibrate the data evaluation under domain shift. Specifically, we introduce
a Dirichlet prior distribution in both local and global models to treat the
prediction as a distribution over the probability simplex and capture both
aleatoric and epistemic uncertainties by using the Dirichlet-based evidential
model. Then we employ the epistemic uncertainty to calibrate the aleatoric
uncertainty. Afterward, we design a diversity relaxation strategy to reduce
data redundancy and maintain data diversity. Extensive experiments and analysis
on five real multi-center medical image datasets demonstrate the superiority of
FEAL over the state-of-the-art active learning methods in federated scenarios
with domain shifts. The code will be available at
https://github.com/JiayiChen815/FEAL.
