The most challenging, yet practical, setting of semi-supervised federated
learning (SSFL) is where a few clients have fully labeled data whereas the
other clients have fully unlabeled data. This is particularly common in
healthcare settings where collaborating partners (typically hospitals) may have
images but not annotations. The bottleneck in this setting is the joint
training of labeled and unlabeled clients as the objective function for each
client varies based on the availability of labels. This paper investigates an
alternative way for effective training with labeled and unlabeled clients in a
federated setting. We propose a novel learning scheme specifically designed for
SSFL which we call Isolated Federated Learning (IsoFed) that circumvents the
problem by avoiding simple averaging of supervised and semi-supervised models
together. In particular, our training approach consists of two parts - (a)
isolated aggregation of labeled and unlabeled client models, and (b) local
self-supervised pretraining of isolated global models in all clients. We
evaluate our model performance on medical image datasets of four different
modalities publicly available within the biomedical image classification
benchmark MedMNIST. We further vary the proportion of labeled clients and the
degree of heterogeneity to demonstrate the effectiveness of the proposed method
under varied experimental settings.

通过提出的分离联合学习（IsoFed）方案，本文研究了在半监督联邦学习中有效训练具有带标签和无标签客户端的方法，并在四种不同模态的医学图像数据集上评估了模型性能，并在不同的实验设置下验证了所提出方法的有效性。