重新思考半监督联邦学习：如何联合训练全标注与全未标注的客户端影像数据
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data
AI总结：
本文针对半监督联邦学习中标注与未标注客户端目标函数不一致的瓶颈，提出隔离联邦学习IsoFed，通过隔离聚合与局部自监督预训练避免简单平均，并在MedMNIST多模态医学影像数据上验证其有效性。
摘要：
半监督联邦学习（SSFL）中最具挑战性但也最贴近实际的环境是：少数客户端拥有完全标注的数据，而其他客户端只有完全未标注的数据。这种情况在医疗健康场景中尤为常见，因为合作方（通常是医院）可能拥有影像但没有标注。该环境下的瓶颈在于标注客户端与未标注客户端的联合训练，因为每个客户端的目标函数会因标签可得性的不同而不同。本文研究了一种在联邦环境下有效训练标注与未标注客户端的替代方法。我们提出了一种专为SSFL设计的新型学习方案，称为隔离联邦学习（IsoFed），通过避免将有监督模型和半监督模型简单平均来规避该问题。具体而言，我们的训练方法包含两部分：（a）对标注客户端模型和未标注客户端模型进行隔离聚合，以及（b）在所有客户端中对隔离后的全局模型进行局部自监督预训练。我们在生物医学图像分类基准MedMNIST中公开可用的四种不同模态的医学影像数据集上评估了模型性能。我们进一步改变标注客户端的比例和异质性程度，以证明所提方法在不同实验设置下的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
