A Federated Explainable AI Model for Breast Cancer Classification

Abstract

Breast cancer diagnosis is a crucial domain where Explainable Artificial Intelligence (XAI) integration holds immense importance. Understanding AI model decisions not only enhances trust but also aids in treatment strategies. However, the need for explainability must address privacy concerns, prompting the exploration of Federated Learning. This study explores the intersection of Explainable AI, Privacy, and Federated Learning in breast cancer diagnosis. Utilizing Wisconsin Diagnostic Breast Cancer Dataset and Wisconsin Breast Cancer Dataset, our results showcase that Federated Learning enhances user privacy while maintaining performance, achieving an accuracy of 97.59% and F1 score of 98.393% in Wisconsin Diagnostic Breast Cancer Dataset using artificial neural networks and 97.14% accuracy and 95.65% F1 score in Wisconsin Breast Cancer Dataset employing XGBoost. By computing SHAP values locally, we maintain explainability while enhancing privacy. Our findings highlight the potential of federated learning in maintaining privacy and explainability, advancing breast cancer diagnosis and treatment.

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