FedEvPrompt reports 77.26% average balanced accuracy on a 6-client federated ISIC2019 binary skin-lesion task by sharing uncertainty-selected attention maps, outperforming FedAvg and FedProx without sharing model parameters.
Evidential deep learning to quantify classification uncertainty
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Evidential Federated Learning for Skin Lesion Image Classification
FedEvPrompt reports 77.26% average balanced accuracy on a 6-client federated ISIC2019 binary skin-lesion task by sharing uncertainty-selected attention maps, outperforming FedAvg and FedProx without sharing model parameters.