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Federated learning facilitates the collaborative learning of a global modelacross multiple distributed medical institutions without centralizing data.Nevertheless, the expensive cost of annotation on local clients remains anobstacle to effectively utilizing local data. To mitigate this issue, federatedactive learning methods suggest leveraging local and global model predictionsto select a relatively small amount of informative local data for annotation.However, existing methods mainly focus on all local data sampled from the samedomain, making them unreliable in realistic medical scenarios with domainshifts among different clients. In this paper, we make the first attempt toassess the informativeness of local data derived from diverse domains andpropose a novel methodology termed Federated Evidential Active Learning (FEAL)to calibrate the data evaluation under domain shift. Specifically, we introducea Dirichlet prior distribution in both local and global models to treat theprediction as a distribution over the probability simplex and capture bothaleatoric and epistemic uncertainties by using the Dirichlet-based evidentialmodel. Then we employ the epistemic uncertainty to calibrate the aleatoricuncertainty. Afterward, we design a diversity relaxation strategy to reducedata redundancy and maintain data diversity. Extensive experiments and analysesare conducted to show the superiority of FEAL over the state-of-the-art activelearning methods and the efficiency of FEAL under the federated active learningframework.

Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts