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.

Many researchers have applied clustering to handle semi-supervised classification of data streams with concept drifts. However, the generalization ability for each specific concept cannot be steadily improved, and the concept drift detection method without considering the local structural information of data cannot accurately detect concept drifts. This paper proposes to solve these problems by BIRCH (Balanced Iterative Reducing and Clustering Using Hierarchies) ensemble and local structure mapping. The local structure mapping strategy is utilized to compute local similarity around each sample and combined with semi-supervised Bayesian method to perform concept detection. If a recurrent concept is detected, a historical BIRCH ensemble classifier is selected to be incrementally updated; otherwise a new BIRCH ensemble classifier is constructed and added into the classifier pool. The extensive experiments on several synthetic and real datasets demonstrate the advantage of the proposed algorithm.