Federated Privacy-Preserving Multi-Modal Deep Learning for Breast Cancer Diagnosis: A Physics-Aware Approach

Breast cancer remains a leading cause of cancer-related mortality among women globally. This study makes one focused primary contribution: a formalized, physics-grounded preprocessing-to-fusion pipeline for multi-modal breast cancer classification that is rigorously validated under both centralized and federated learning conditions. Patient-wise stratified 5-fold cross-validation was applied across Ultrasound (BUSI, n=780), Dynamic Contrast-Enhanced MRI (DUKE, n=922), and Mammography (CBIS-DDSM, n=400). Per-modality models achieved 92.50±1.2%, 90.63±1.5%, and 92.00±1.3% accuracy (McNemar’s p<0.05 vs. baselines). Weighted late-fusion achieved 93.10±1.1% (p=0.031 vs. best individual modality). A five-algorithm FL comparison (FedAvg, FedProx, SCAFFOLD, FedNova, FP16-FedAvg) under IID and non-IID (Dirichlet α=0.5) conditions is provided with per-round training time, communication time, per-round latency, and cumulative bandwidth. FP16 transmission reduced bandwidth from 8.14 GB to 1.23 GB (−84.9%, p=0.74 vs. FP32). SCAFFOLD achieved the best non-IID accuracy (90.50%). All design choices are validated by ablation experiments with McNemar’s test and Cohen’s h effect sizes.