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）是一种协作训练范式，通过消除敏感数据交换，转而依赖客户端与服务器间模型参数的交换，实现跨机构模型的隐私保护学习。尽管已有针对客户端模型聚合方式的独立研究，且近期涉及ImageNet预训练优势的探讨，但学界对联邦过程中所选架构的影响，以及上述各要素间的内在关联仍缺乏理解。为此，我们首次开展架构-初始化-聚合联合研究（ARIA），并在多项医学图像分类任务中建立基准。研究发现，与当前实践相悖，需协同选择ARIA各要素方能实现最优性能。我们的结果还揭示了不同任务下各要素的优选方案、归一化层的影响及自监督预训练的效用，为设计面向联邦学习的专属架构与训练流程指明了潜在方向。