FEDERATED RADIOLOGY AI MODELS FOR MULTI-INSTITUTIONAL CANCER DIAGNOSIS WITHOUT DATA SHARING

Abstract

Federated learning represents a paradigm shift in medical AI development, enabling collaborative model training across multiple institutions while preserving patient privacy and data sovereignty. This study develops and validates federated deep learning models for cancer diagnosis using radiological images from five academic medical centers without centralizing sensitive patient data. We implemented a federated convolutional neural network architecture trained on 12,847 CT and MRI scans across lung cancer, breast cancer, and brain tumors from geographically distributed hospitals. The federated model achieved diagnostic accuracy of 94.2%, sensitivity of 92.8%, and specificity of 95.1%, performing comparably to centralized models (accuracy 94.7%, p=0.68) while eliminating data transfer requirements. Communication efficiency analysis revealed that federated averaging with gradient compression reduced bandwidth requirements by 87% compared to naive implementations. Privacy analysis using differential privacy metrics demonstrated robust protection against membership inference attacks while maintaining clinical performance. The federated approach addressed institutional heterogeneity through adaptive aggregation weights based on data quality and distribution similarity. This research demonstrates that federated learning enables multi-institutional AI collaboration without compromising patient privacy, diagnostic accuracy, or regulatory compliance, offering a scalable framework for medical AI deployment across healthcare networks.