This paper proposes a federated transfer learning approach for privacy-preserving breast cancer classification. The method combines federated learning (FL) with transfer learning to address the challenges of limited labeled data and data privacy in collaborative healthcare settings. The approach uses pre-trained ResNet as a feature extractor, fine-tunes the higher layers on breast cancer datasets from multiple medical centers, and incorporates domain adversarial training to mitigate domain shift challenges caused by variations in data distributions across institutions. The model is trained collaboratively across three medical centers without sharing raw data, ensuring data privacy while leveraging the comprehensive image representations learned from large-scale datasets like ImageNet. The proposed method achieves a classification accuracy of 98.8% and a computational time of 12.22 seconds, demonstrating significant improvements in classification accuracy and model generalization. The study highlights the potential of federated transfer learning in improving breast cancer classification performance while maintaining data privacy in a collaborative healthcare environment. The methodology involves pre-processing and data augmentation to standardize image inputs, followed by transfer learning with ResNet fine-tuning and domain adversarial training within the federated learning framework. The results show that the proposed approach effectively addresses data scarcity, overfitting, domain shift, and data privacy concerns, offering a promising solution for privacy-preserving breast cancer classification in a federated learning setting.This paper proposes a federated transfer learning approach for privacy-preserving breast cancer classification. The method combines federated learning (FL) with transfer learning to address the challenges of limited labeled data and data privacy in collaborative healthcare settings. The approach uses pre-trained ResNet as a feature extractor, fine-tunes the higher layers on breast cancer datasets from multiple medical centers, and incorporates domain adversarial training to mitigate domain shift challenges caused by variations in data distributions across institutions. The model is trained collaboratively across three medical centers without sharing raw data, ensuring data privacy while leveraging the comprehensive image representations learned from large-scale datasets like ImageNet. The proposed method achieves a classification accuracy of 98.8% and a computational time of 12.22 seconds, demonstrating significant improvements in classification accuracy and model generalization. The study highlights the potential of federated transfer learning in improving breast cancer classification performance while maintaining data privacy in a collaborative healthcare environment. The methodology involves pre-processing and data augmentation to standardize image inputs, followed by transfer learning with ResNet fine-tuning and domain adversarial training within the federated learning framework. The results show that the proposed approach effectively addresses data scarcity, overfitting, domain shift, and data privacy concerns, offering a promising solution for privacy-preserving breast cancer classification in a federated learning setting.