
The author presents a novel Federated Semi-supervised Learning framework, FedDure, to tackle data imbalances by introducing dual regulators. The approach involves a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg) to adaptively update the local model based on unique client data distributions.


coremsg

addressing-data-imbalances-in-federated-semi-supervised-learning-with-dual-regulators


Addressing Data Imbalances in Federated Semi-supervised Learning with Dual Regulators


title_rewrite


Proposing a novel federated semi-supervised learning framework for medical image segmentation with intra-client and inter-client consistency.


federated-semi-supervised-learning-for-medical-image-segmentation-with-intra-client-and-inter-client-consistency


Federated Semi-supervised Learning for Medical Image Segmentation with Intra-client and Inter-client Consistency
