Federated semi-supervised learning (FSSL) aims to derive a global model by
training fully-labeled and fully-unlabeled clients or training partially
labeled clients. The existing approaches work well when local clients have
independent and identically distributed (IID) data but fail to generalize to a
more practical FSSL setting, i.e., Non-IID setting. In this paper, we present a
Random Sampling Consensus Federated learning, namely RSCFed, by considering the
uneven reliability among models from fully-labeled clients, fully-unlabeled
clients or partially labeled clients. Our key motivation is that given models
with large deviations from either labeled clients or unlabeled clients, the
consensus could be reached by performing random sub-sampling over clients. To
achieve it, instead of directly aggregating local models, we first distill
several sub-consensus models by random sub-sampling over clients and then
aggregating the sub-consensus models to the global model. To enhance the
robustness of sub-consensus models, we also develop a novel distance-reweighted
model aggregation method. Experimental results show that our method outperforms
state-of-the-art methods on three benchmarked datasets, including both natural
and medical images. The code is available at
this https URL

该研究提出了一种考虑不同类型客户端的随机采样一致性联邦学习算法，即 RSCFed，通过子取样一致性模型的蒸馏和重加权模型聚合来实现，该方法在自然和医学图像等三个基准数据集上表现优于现有方法。