论文标题
联合肿瘤分割挑战的强大学习方案
Robust Learning Protocol for Federated Tumor Segmentation Challenge
论文作者
论文摘要
在这项工作中，我们为联邦肿瘤分割挑战（FETS 2022）编排联合学习（FL）过程来设计强大而有效的学习方案。启用FET设置的FL主要是由于合作者之间的数据异质性和培训的沟通成本。为了应对这些挑战，我们提出了强大的学习协议（Rolepro），该协议是服务器端自适应优化（例如服务器端ADAM）和明智参数（权重）聚合方案（例如自适应加权聚合）的组合。 Rolepro采用了两阶段的方法，其中第一阶段由平均的Vanilla组成，而第二阶段由使用复杂的重新加权的明智聚合方案组成，所有这些方案都在服务器上具有适应性优化算法的情况下。我们从广泛的实验到两个阶段的调整学习率中获取见解。
In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging mainly due to data heterogeneity among collaborators and communication cost of training. To tackle these challenges, we propose Robust Learning Protocol (RoLePRO) which is a combination of server-side adaptive optimisation (e.g., server-side Adam) and judicious parameter (weights) aggregation schemes (e.g., adaptive weighted aggregation). RoLePRO takes a two-phase approach, where the first phase consists of vanilla Federated Averaging, while the second phase consists of a judicious aggregation scheme that uses a sophisticated reweighting, all in the presence of an adaptive optimisation algorithm at the server. We draw insights from extensive experimentation to tune learning rates for the two phases.
