Privacy-Preserving Breast Cancer Detection using Federated Learning with Ensemble Models

Sudhanshu Baliram Chavan; Rajaselvan C

Privacy-Preserving Breast Cancer Detection using Federated Learning with Ensemble Models

Author(s)	Mr. Sudhanshu Baliram Chavan, Dr. Rajaselvan C
Country	India
Abstract	Breast cancer is one of the most prevalent causes of cancer-related mortality among women worldwide. Machine learning techniques have significantly improved diagnostic accuracy; however, centralized learning approaches require aggregation of sensitive healthcare records, creating privacy and regulatory concerns. This research proposes a privacy-preserving breast cancer detection framework based on Federated Learning (FL) and ensemble machine learning models. The framework enables multiple healthcare institutions to collaboratively train predictive models without sharing raw patient data. The Breast Cancer Wisconsin Diagnostic Dataset (BCWD) was used for experimental evaluation. Ensemble classifiers including AdaBoost, Decision Tree (DT), Extra Trees Classifier (ETC), and Linear Discriminant Analysis (LDA) were evaluated using multiple train-test splits, cross-validation techniques, feature selection methods, and hyperparameter optimization strategies. Experimental results demonstrate that AdaBoost achieved the best overall performance, reaching 96.49% accuracy with high precision and recall. Federated learning preserved privacy while maintaining strong predictive capability. Comparative analysis further demonstrated improvements over traditional classification approaches. The proposed framework offers a secure and effective solution for healthcare analytics in distributed environments.
Keywords	Federated Learning, Breast Cancer Detection, Ensemble Learning, Privacy Preservation, AdaBoost, Healthcare Analytics
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-12

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Privacy-Preserving Breast Cancer Detection using Federated Learning with Ensemble Models

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