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.
Cite
@article{arxiv.2203.06338,
title = {Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation},
author = {Pengfei Guo and Dong Yang and Ali Hatamizadeh and An Xu and Ziyue Xu and Wenqi Li and Can Zhao and Daguang Xu and Stephanie Harmon and Evrim Turkbey and Baris Turkbey and Bradford Wood and Francesca Patella and Elvira Stellato and Gianpaolo Carrafiello and Vishal M. Patel and Holger R. Roth},
journal= {arXiv preprint arXiv:2203.06338},
year = {2022}
}