Recent model recovery methods in federated unlearning (FUL) either rely on additional communication with the remaining clients or require large amounts of high-quality data from the server for training, overlooking scenarios with limited data resources. Currently, Contrastive Language-Image Pretraining (CLIP) has demonstrated remarkable performance across a wide range of tasks, particularly excelling in few-shot learning scenarios. In this paper, inspired by CLIP, we explore the scenario of few-shot knowledge distillation and propose CLIP-Guided Few-shot Knowledge Distillation (CGKD) for Model Recovery in FUL. CGKD mainly consists of three components: 1) The unlearning module constructs the unlearning model by erasing all historical contributions of the target client, and this model is treated as the student model; 2) Fine-tuning the pre-trained CLIP model using few-shot data from the server side to obtain a more robust teacher model (CLIP*); and 3) Model recovery is achieved through knowledge distillation, leveraging the rich visual and semantic knowledge of CLIP* to enhance the student model’s understanding of image semantic context, thereby improving the performance of the unlearning model. Extensive experimental results demonstrate that CGKD outperforms the compared FUL method in recovery performance across four standard datasets, validating the effectiveness of our approach.

Existing convolutional neural network (CNN) methods primarily depend on first-order feature modeling, which makes it challenging to effectively capture higher-order features in breast cancer histopathological images. Additionally, due to the limitations of the receptive field, CNNs have difficulty capturing long-range dependencies, thereby limiting the integration of global information. To address this, inspired by the strengths of high-order statistical features and extended receptive fields in visual tasks, this study proposes a novel high-order receptive field network (HoRFNet). Specifically, HoRFNet expands the receptive field and improves the model’s contextual awareness of pathological tissue structures by introducing a multi-branch convolutional structure with convolution kernels of varying sizes, along with dilated convolution layers. Additionally, HoRFNet integrates a matrix power normalization strategy in the covariance pooling module to model the global interactions between convolutional features, thereby improving the higher-order representation of complex textures and structural relationships in tissue images. The BreakHis dataset shows that HoRFNet achieves an image level classification accuracy of 99.50% and a patient level classification accuracy of 99.23%, significantly outperforming existing methods and demonstrating its effectiveness.

Breast cancer histopathological image classification remains a challenging task because reliable diagnosis depends on both fine-grained local lesion characteristics and multi-scale global tissue structures. However, current deep learning approaches often face challenges in effectively integrating these complementary cues, particularly in the presence of staining variations, ambiguous lesion boundaries, and limited annotated datasets. To address these challenges, we propose a novel method called UNI-Phase-Dual Network (UPDNet). This approach enhances the detection of stable lesion boundaries and subtle patterns by incorporating phase congruency, while combining it with global tissue information using the UNI foundation model. The method utilizes two branches to process features from different perspectives, one focusing on fine details and the other capturing broader context. Additionally, we apply a fine-tuning strategy that improves generalization and reduces overfitting in scenarios with small datasets. Experiments on three widely used breast cancer datasets, BRACS, BreakHis, and BACH, demonstrate that UPDNet significantly outperforms existing methods. Specifically, on the 7-class BRACS task, UPDNet achieves 68.58% accuracy, which is a 2.21% improvement over previous methods, and an increase of 1.48% in the weighted F1 score. These results demonstrate the strong potential of UPDNet in breast cancer histopathological image classification.