Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data and instead relying on the exchange of model parameters between the clients and a server. Despite individual studies on how client models are aggregated, and, more recently, on the benefits of ImageNet pre-training, there is a lack of understanding of the effect the architecture chosen for the federation has, and of how the aforementioned elements interconnect. To this end, we conduct the first joint ARchitecture-Initialization-Aggregation study and benchmark ARIAs across a range of medical image classification tasks. We find that, contrary to current practices, ARIA elements have to be chosen together to achieve the best possible performance. Our results also shed light on good choices for each element depending on the task, the effect of normalisation layers, and the utility of SSL pre-training, pointing to potential directions for designing FL-specific architectures and training pipelines.

翻译：联邦学习（FL）是一种协同训练范式，通过消除敏感数据交换，转而依赖客户端与服务器之间交换模型参数，实现隐私保护的跨机构模型训练。尽管已有关于客户端模型聚合方式的独立研究，以及近期关于ImageNet预训练优势的探讨，但对于联邦架构选择的影响以及上述各要素之间的相互关联仍缺乏认知。为此，我们首次开展架构-初始化-聚合联合研究（ARIA），并在多种医学图像分类任务中建立基准测试。研究发现，与当前实践相反，需协同选择ARIA各要素方能获得最优性能。我们的结果还揭示了各要素的任务适配选择、归一化层效应以及自监督预训练的实用价值，为设计联邦学习专用架构与训练流程指明了潜在方向。