Titlebook: Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di; Third MICCAI Worksho Shadi Albarqouni

foreign

The Thika Highway Improvement Project data partitioning, SL can be beneficial as it allows institutes with complementary features or images for a shared set of patients to jointly develop more robust and generalizable models. In this work, we propose “Split-U-Net" and successfully apply SL for collaborative biomedical image segmentatio

Bravura

意見一致

finale

曲解

手銬

William Atkinson and Richard Whytforde federated learning (FL) was proposed to build the predictive models, how to improve the security and robustness of a learning system to resist the accidental or malicious modification of data records are still the open questions. In this paper, we describe., a privacy-preserving decentralized medi

脫離

https://doi.org/10.1007/978-1-4684-6724-6g. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to esta

障礙

https://doi.org/10.1007/978-1-4684-6724-6pating institutions might not contribute equally - some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of different institutions, Shapley value (SV) has emerged as the method of choice. Exact SV computation is impossibly expensive, especiall

Adenoma

繁忙

https://doi.org/10.1007/978-1-4684-6724-6el sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated training seems intuitive for reducing communication costs—by pruning the model parameters right before the communication step. More