Federated transfer learning with differential privacy for multi-omics survival analysis.

Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size, making the integration of multi-omics data for survival analysis of a specific cancer particularly challenging. One common strategy is to share multi-omics data from other related cancers across multiple institutions and leverage the abundant data from these cancers to enhance survival predictions for the target cancer. However, due to data privacy and data-sharing regulations, it is challenging to aggregate multi-omics data of related cancers from multiple institutions into a centralized database to learn more accurate and robust models for the target cancer. To address the limitation, we propose a multi-omics survival prediction model with self-attention mechanism (MOSAHit), trained within a federated transfer learning framework with differential privacy. This approach enables the learning of a more robust multi-omics survival prediction model for a local target cancer with limited training data by effectively leveraging multi-omics data of related cancers distributed across multiple institutions while preserving individual privacy. Results from the comprehensive experiments on real-world datasets show that the proposed method effectively alleviates data insufficiency and significantly improves the generalization performance of multi-omics survival prediction model for a target cancer while avoiding the direct sharing of multi-omics data for related cancers.