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Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
[article]
2022
arXiv
pre-print
Preserved Fulltext
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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