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

Pengfei Guo, Dong Yang, Ali Hatamizadeh, An Xu, Ziyue Xu, Wenqi Li, Can Zhao, Daguang Xu, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood (+5 others)
2022 arXiv   pre-print
The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset as well as two real-world medical image segmentation datasets for COVID-19 lesion segmentation  ...  in chest CT and pancreas segmentation in abdominal CT.  ...  Then, experiments are conducted on two multi-institutional medical image segmentation datasets (i.e., COVID-19 lesion segmentation and pancreas segmentation) to investigate the real-world potential of  ... 
arXiv:2203.06338v1 fatcat:ig3amhtkkzbq3jnk5m6pnowj3e

Optimality and Stability in Federated Learning: A Game-theoretic Approach [article]

Kate Donahue, Jon Kleinberg
2021 arXiv   pre-print
In this work, we motivate and define a notion of optimality given by the average error rates among federating agents (players).  ...  First, we show that for some regions of parameter space, all stable arrangements are optimal (Price of Anarchy equal to 1).  ...  Auto-fedavg: Learnable federated averaging for multi-institutional medical image segmentation. arXiv preprint arXiv:2104.10195, 2021. Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov.  ... 
arXiv:2106.09580v1 fatcat:fcnrs62tbfbivo6jceyzdby66m