Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

no code implementations • 12 Mar 2022 • Pengfei Guo, Dong Yang, Ali Hatamizadeh, An Xu, Ziyue Xu, Wenqi Li, Can Zhao, Daguang Xu, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Vishal M. Patel, Holger R. Roth

Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.

Federated Learning Hyperparameter Optimization +7

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation

no code implementations • 20 Apr 2021 • Yingda Xia, Dong Yang, Wenqi Li, Andriy Myronenko, Daguang Xu, Hirofumi Obinata, Hitoshi Mori, Peng An, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Anna Ierardi, Alan Yuille, Holger Roth

In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.

Federated Learning Image Segmentation +3

Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan

no code implementations • 23 Nov 2020 • Dong Yang, Ziyue Xu, Wenqi Li, Andriy Myronenko, Holger R. Roth, Stephanie Harmon, Sheng Xu, Baris Turkbey, Evrim Turkbey, Xiaosong Wang, Wentao Zhu, Gianpaolo Carrafiello, Francesca Patella, Maurizio Cariati, Hirofumi Obinata, Hitoshi Mori, Kaku Tamura, Peng An, Bradford J. Wood, Daguang Xu

To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.

Federated Learning Management