Auto-FedRL：多机构医学图像分割的联邦超参数优化
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
摘要：
联邦学习（FL）是一种分布式机器学习技术，它支持协作模型训练，同时避免显式数据共享。FL算法固有的隐私保护特性使其对医学领域特别有吸引力。然而，在异构客户端数据分布的情况下，标准FL方法是不稳定的，并且需要密集的超参数调整以实现最佳性能。传统的超参数优化算法在现实世界的FL应用中是不切实际的，因为它们涉及大量的训练试验，这通常是有限的计算预算所无法负担的。在这项工作中，我们提出了一个有效的基于强化学习（RL）的联邦超参数优化算法，称为Auto-FedRL，其中在线RL代理可以根据当前的训练进度动态调整每个客户端的超参数。进行了大量的实验，以研究不同的搜索策略和RL代理。在CIFAR-10数据集的异构数据分割以及胸部CT中的COVID-19病变分割和腹部CT中的胰腺分割的两个真实世界医学图像分割数据集上验证了所提出的方法的有效性。
英文摘要：
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.
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