Applying Federated Learning For Breast Cancer Prediction

Abstract: This project aims Breast cancer remains a significant global health challenge, necessitating advancements in predictive modeling to enable early detection and personalized treatment. Traditional centralized machine learning approaches often face privacy concerns, especially when dealing with sensitive medical data. Federated Learning (FL) emerges as a promising solution, allowing model training across decentralized devices without sharing raw data. This project explores the implementation of Federated Learning for Breast Cancer Prediction, aiming to improve both privacy and prediction accuracy. The federated learning framework involves collaboration among multiple healthcare institutions, each possessing a subset of breast cancer patient data. The model is trained locally on each institution's data, and explored for decentralized model training on distributed data sources, preserving privacy and security. The trained federated model is evaluated and saved for deployment. Further research in this area holds promise for revolutionizing healthcare delivery worldwide.

Keywords: Breast cancer prediction, Artificial intelligence, Deep learning, Centralized learning, Decentralized learning, Image-based analysis, Machine learning, Healthcare applications.