Paper Info
Title
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation.
Abstract
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordable with limited compute budgets. In this work, we propose an efficient reinforcement learning (RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL, in which an online RL agent can dynamically adjust hyperparameters of each client based on the current training progress. Extensive experiments are conducted to investigate different search strategies and RL agents. 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.
Year
DOI
Venue
2022
10.1007/978-3-031-19803-8_26
European Conference on Computer Vision
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
17
Name
Order
Citations
PageRank
Pengfei Guo100.68
Dong Yang24711.05
Ali Hatamizadeh333.45
An Xu422.77
Ziyue Xu559735.50
Wenqi Li630920.82
Can Zhao701.01
Daguang Xu85014.28
Stephanie Harmon9152.17
Evrim Turkbey1060.94
Baris Turkbey1100.34
Bradford Wood1200.34
Francesca Patella1360.94
Elvira Stellato1400.34
Gianpaolo Carrafiello1500.34
Vishal M. Patel162251110.69
Holger R. Roth1751.10