Federated Computational Graph for Privacy-Preserving Oncology Planning

The system segments the centralized computational model into distributed local models across multiple institutions. Each institution trains and maintains its own local AI model on its private data, while only model parameters and gradients are shared through secure federated learning protocols. This segmentation enables computational power to be distributed across institutions without centralizing sensitive biological data, thus improving computational capability while preserving data privacy.

The patent introduces a federated learning intermediary layer that mediates between diverse data sources (imaging, genomics, electronic health records) and the analytical models. This intermediary handles data standardization, privacy-preserving computation, and coordinated training across institutions, thereby enabling real-time spatiotemporal analysis for precise tumor evolution tracking without requiring direct integration of complex heterogeneous data systems.

Instead of sharing actual sensitive genomic data across institutions, the system creates and shares copies in the form of trained model parameters, gradients, and synthetic data representations. These copies enable cross-institutional research collaboration and genomic intervention planning while the original sensitive data remains securely localized at each institution, thus improving research versatility without compromising data security or regulatory compliance.