  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.
