A label-efficient paradigm in computer vision is based on self-supervised
contrastive pre-training on unlabeled data followed by fine-tuning with a small
number of labels. Making practical use of a federated computing environment in
the clinical domain and learning on medical images poses specific challenges.
In this work, we propose FedMoCo, a robust federated contrastive learning (FCL)
framework, which makes efficient use of decentralized unlabeled medical data.
FedMoCo has two novel modules: metadata transfer, an inter-node statistical
data augmentation module, and self-adaptive aggregation, an aggregation module
based on representational similarity analysis. To the best of our knowledge,
this is the first FCL work on medical images. Our experiments show that FedMoCo
can consistently outperform FedAvg, a seminal federated learning framework, in
extracting meaningful representations for downstream tasks. We further show
that FedMoCo can substantially reduce the amount of labeled data required in a
downstream task, such as COVID-19 detection, to achieve a reasonable
performance.

本研究提出了一个名为 FedMoCo 的鲁棒性联邦对比学习（FCL）框架，该框架在临床领域的分布式无标签医学数据上进行元数据传输和自适应聚合，并在 COVID-19 检测等下游任务中显著减少有标签数据需求，从而相对于传统联邦学习框架 FedAvg，更能在从医学图像中提取有意义的表示方面表现良好。