Federated Learning for Weakly Supervised Nuclei Segmentation via Style Perturbation and Clustering

Nuclei segmentation is crucial for cancer di-agnosis but faces high annotation costs due to dense nu-clei distribution. Weakly supervised learning with point an-notations alleviates this burden, yet single-center data is limited, and centralized datasets are hindered by privacy concerns. Federated learning enables multi-institution col-laboration while preserving privacy, but non-IID data dis-tribution—particularly style heterogeneity from staining and equipment variations—complicates model aggregation. In this paper, we propose Federated learning with Style Perturbation and Clustering (FedSPC), a novel framework that integrates a Federated Style Perturbation (FedSP) model and a Federated Style Clustering (FedSC) strategy. During training, FedSP applies style adversarial perturbation to extract and adapt local style features, reducing local style bias. Meanwhile, FedSC groups clients by style similarity and adjusts aggre-gation weights based on intra-group performance, mitigating fairness propagation bias. FedSPC overcomes pathological image style heterogeneity through the combined use of FedSP and FedSC, delivering a practical federated learning solution for medical imaging. Evaluated against existing federated weakly supervised frameworks, conventional methods, and aggregation schemes, our approach significantly outperforms alternatives in nuclei segmentation tasks. Experiments con-firm FedSPC’s superiority in handling style diversity and improving segmentation accuracy under federated settings.