Nuclei segmentation is crucial for cancer diagnosis but faces high annotation costs due to dense nuclei distribution. Weakly supervised learning with point annotations alleviates this burden, yet single-center data are limited, and centralized datasets are hindered by privacy concerns. Federated Learning (FL) enables multi-institution collaboration while preserving privacy, but non-Independent and Identically Distributed (non-IID) data distribution—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 aggregation 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 FL 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 confirm FedSPC’s superiority in handling style diversity and improving segmentation accuracy under federated settings. Our code is available at https://github.com/Qyizos/FedSPC.