Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on a server during the FL process. However, non-identical data distribution across clients, known as the non-i.i.d problem in FL, could make this assumption for setting fixed aggregation weights sub-optimal. In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models. We disentangle the parameter set into two parts, local model parameters and global aggregation parameters, and update them iteratively with a communication-efficient algorithm. We first show the validity of our approach by outperforming state-of-the-art FL methods for image recognition on a heterogeneous data split of CIFAR-10. Furthermore, we demonstrate our algorithm's effectiveness on two multi-institutional medical image analysis tasks, i.e., COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT. 中文翻译： 联合学习（FL）支持协作模型训练，同时保留每个参与者的隐私，这对医学领域特别有利。FedAvg是一种标准算法，该算法使用固定权重（通常来自每个客户端的数据集大小）来在FL过程中聚合服务器上的分布式学习模型。但是，跨客户端的不完全相同的数据分布（在FL中称为“非自发问题”）可能会使设置固定聚合权重的假设不理想。在这项工作中，我们设计了一种新的数据驱动方法，即Auto-FedAvg，可根据数据孤岛上的数据分布和模型的当前训练进度来动态调整聚合权重。我们将参数集分解为两部分，局部模型参数和全局聚集参数，并使用通信效率高的算法进行迭代更新。我们首先通过在CIFAR-10的异构数据拆分上优于最新的FL方法进行图像识别来证明我们方法的有效性。此外，我们证明了我们的算法在两个多机构医学图像分析任务上的有效性，即胸部CT的COVID-19病变分割和腹部CT的胰腺分割。