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Found 3 papers, 1 papers with code
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation.
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
The statistical heterogeneity (e. g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model.
BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain Adaptation
Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase.
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