Privacy-Aware Federated Graph Neural Networks for Adaptive and Explainable Cancer Drug Personalization

Personalized cancer treatment remains challenging due to the complexity of genomic data and variability in drug responses. Previous federated learning (FL) approaches handled distributed patient data to preserve privacy but treated genomic and pharmacological features as flat, tabular inputs, limiting the ability to capture gene–drug interactions. In this study, we propose a Graph Neural Network (GNN)-based framework, FedGraphOnco, which models patient-specific gene–drug interactions as structured graphs, enabling the network to learn complex relational patterns that are difficult or impractical for FL-only models. Attention mechanisms and SHapley Additive exPlanations (SHAP) are incorporated to provide interpretable insights into important genes, pathways, and drug interactions, increasing clinical trust. Using the GDSC dataset with gene expression, mutation status, copy number variation, and IC50 drug responses, the model demonstrates high predictive accuracy (Pearson correlation = 0.85, RMSE = 2.6, MAE = 1.9, dosage deviation = 2.8%), robustness to noise and non-IID data, and adaptive, personalized dosage recommendations. The approach highlights the advantages of combining privacy-preserving FL, GNNs, multi-omics data integration, explainability, and adaptive dosing, offering a scalable and interpretable solution for precision oncology.