FedHAC: Towards Robust Federated Multi-Lesion Segmentation With Heterogeneous Annotation Completeness

Federated learning (FL) has emerged as a promising paradigm for collaborative medical image segmentation across institutions while preserving data privacy. Despite great efforts in addressing cross-client annotation heterogeneity FL, the prevalent annotation completeness heterogeneity in clinical practice due to varying diagnostic priorities has been completely overlooked, hindering the deployment of FL. In this paper, we formulate such a challenge and propose FedHAC for incompleteness-robust medical image segmentation. FedHAC consists of three modules, i.e., Global Class Prototype Alignment (GCPA), Annotation Completeness-Aware Aggregation (ACAA), and GMM-driven Progressive Correction (GPC). Specifically, GCPA constructs a noise-resilient warm-up model through proximal-term regularization and prototype alignment. ACAA estimates client-wise annotation completeness and dynamically prioritizes high-quality clients. GPC groups clients into "noisy" and "clean" via GMM for progressive annotation correction to minimize error propagation. Extensive comparison experiments and ablation studies on public datasets demonstrate the superiority of FedHAC over state-of-the-art methods under various levels of annotation incompleteness.