Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. The statistical heterogeneity (e.g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model. Weakly supervised segmentation, which uses sparsely-grained (i.e., point-, bounding box-, scribble-, block-wise) supervision, is increasingly being paid attention to due to its great potential of reducing annotation costs. However, there may exist label heterogeneity, i.e., different annotation forms across sites. In this paper, we propose a novel personalized FL framework for medical image segmentation, named FedICRA, which uniformly leverages heterogeneous weak supervision via adaptIve Contrastive Representation and Aggregation. Concretely, to facilitate personalized modeling and to avoid confusion, a channel selection based site contrastive representation module is employed to adaptively cluster intra-site embeddings and separate inter-site ones. To effectively integrate the common knowledge from the global model with the unique knowledge from each local model, an adaptive aggregation module is applied for updating and initializing local models at the element level. Additionally, a weakly supervised objective function that leverages a multiscale tree energy loss and a gated CRF loss is employed to generate more precise pseudo-labels and further boost the segmentation performance. Through extensive experiments on two distinct medical image segmentation tasks of different modalities, the proposed FedICRA demonstrates overwhelming performance over other state-of-the-art personalized FL methods. Its performance even approaches that of fully supervised training on centralized data. Our code and data are available at https://github.com/llmir/FedICRA.

翻译：联邦学习（FL）使多个站点能够在不损害数据隐私和安全的情况下协同训练强大的深度模型。统计异质性（例如非独立同分布数据和域偏移）是FL中的主要障碍，会损害全局模型的泛化性能。弱监督分割利用稀疏粒度（即点级、边界框级、涂鸦级、块级）监督，因其显著降低标注成本的潜力而日益受到关注。然而，可能存在标签异质性，即不同站点间采用不同的标注形式。本文提出一种新颖的个性化联邦学习框架用于医学图像分割，名为FedICRA，该框架通过自适应对比表征与聚合（AdaptIve Contrastive Representation and Aggregation）统一利用异质性弱监督。具体而言，为促进个性化建模并避免混淆，采用基于通道选择的站点对比表征模块自适应地聚类站点内嵌入并分离站点间嵌入。为有效整合全局模型的共同知识与各局部模型的独特知识，应用自适应聚合模块在元素层面更新和初始化局部模型。此外，采用结合多尺度树能量损失与门控条件随机场损失的弱监督目标函数，以生成更精确的伪标签并进一步提升分割性能。通过在两个不同模态的医学图像分割任务上进行广泛实验，所提出的FedICRA相较于其他最先进的个性化联邦学习方法展现出卓越性能，其性能甚至接近全监督集中式数据训练的效果。我们的代码与数据公开于https://github.com/llmir/FedICRA。