SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging.
SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging.
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SplitAVG：一种用于医学成像的异构感知联合深度学习方法。
DOI：
10.1109/jbhi.2022.3185956
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发表时间：
2022-09
影响因子：
7.7
通讯作者：
中科院分区：
文献类型：
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联合学习是一种新兴的研究范式，可以在不共享患者数据的情况下协作训练深度学习模型。然而，来自不同机构的数据通常是异构的，这可能会降低使用联邦学习训练的模型的性能。在这项研究中，我们提出了一种新的异质性感知的联邦学习方法，SplitAVG，以克服联邦学习中的数据异质性的性能下降。与以前需要复杂的启发式训练或超参数调整的联邦方法不同，我们的SplitAVG利用简单的网络分割和特征映射拼接策略来鼓励联邦模型训练目标数据分布的无偏估计器。我们将SplitAVG与七种最先进的联邦学习方法进行了比较，使用集中托管的训练数据作为一套合成和真实世界联邦数据集的基线。我们发现，使用所有比较联邦学习方法训练的模型的性能随着数据异构程度的增加而显着下降。相比之下，SplitAVG方法在所有异质设置下实现了与基线方法相当的结果，即在高度异质的数据分区上，它分别在糖尿病视网膜病变二元分类数据集和骨龄预测数据集中实现了由基线获得的96.2%的准确度和110.4%的平均绝对误差。我们得出结论，SplitAVG方法可以有效地克服跨机构的数据分布的可变性的性能下降。实验结果还表明，SplitAVG可以适应不同的基础卷积神经网络（CNN），并推广到各种类型的医学成像任务。该代码可在https://github.com/zm17943/SplitAVG上公开获取。
Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different institutions are usually heterogeneous across institutions, which may reduce the performance of models trained using federated learning. In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning. Unlike previous federated methods that require complex heuristic training or hyper parameter tuning, our SplitAVG leverages the simple network split and feature map concatenation strategies to encourage the federated model training an unbiased estimator of the target data distribution. We compare SplitAVG with seven state-of-the-art federated learning methods, using centrally hosted training data as the baseline on a suite of both synthetic and real-world federated datasets. We find that the performance of models trained using all the comparison federated learning methods degraded significantly with the increasing degrees of data heterogeneity. In contrast, SplitAVG method achieves comparable results to the baseline method under all heterogeneous settings, that it achieves 96.2% of the accuracy and 110.4% of the mean absolute error obtained by the baseline in a diabetic retinopathy binary classification dataset and a bone age prediction dataset, respectively, on highly heterogeneous data partitions. We conclude that SplitAVG method can effectively overcome the performance drops from variability in data distributions across institutions. Experimental results also show that SplitAVG can be adapted to different base convolutional neural networks (CNNs) and generalized to various types of medical imaging tasks. The code is publicly available at https://github.com/zm17943/SplitAVG.