Federated Multi-Omics Integration

A hub-and-spoke architecture where each institution retains its own genomics, proteomics, and transcriptomics data locally. A central aggregation server orchestrates training rounds by distributing a global model, collecting only encrypted gradient updates or model weights.

• Each silo trains on its complete multi-omics stack locally
• Only mathematical updates—never raw sequence reads or protein expression matrices—leave the institution
• Supports horizontal federated learning (same omics layers across sites) and vertical federated learning (different omics layers across sites)

Each participating site maintains independent neural network branches for each omics layer before fusion. A genomic encoder processes variant call format files, a proteomic encoder handles mass spectrometry intensities, and a transcriptomic encoder processes RNA-seq count matrices.

• Encoders are trained locally and never shared in raw form
• Enables intermediate fusion where modality-specific embeddings are combined at a hidden layer
• Supports heterogeneous data formats across institutions without requiring a common schema

A self-supervised learning strategy that aligns representations of the same patient across different omics layers without labels. Local models maximize agreement between a patient's genomic embedding and their proteomic embedding while pushing apart embeddings from different individuals.

• Works with unlabeled data, critical for rare disease cohorts
• Builds a shared joint embedding space across modalities before supervised fine-tuning
• Reduces the statistical heterogeneity introduced by site-specific batch effects

Before transmitting model updates, each site applies differentially private stochastic gradient descent (DP-SGD) by clipping gradients and injecting calibrated Gaussian noise. This provides a mathematical guarantee that an adversary cannot determine whether any single patient's molecular profile was included in training.

• Privacy budget (ε) is tracked and audited across training rounds
• Balances the privacy-utility tradeoff: lower epsilon values provide stronger guarantees but may reduce model accuracy
• Compatible with secure aggregation protocols for defense-in-depth

Clinical reality dictates that not every patient has all omics layers available. Federated multi-omics systems employ modality dropout during local training and generative imputation modules that infer missing layers from available ones.

• A patient with only genomics and transcriptomics can still contribute to training
• Imputation models are trained federatedly, learning to reconstruct missing proteomics from gene expression patterns
• Prevents systematic exclusion of institutions with incomplete data collection pipelines

The end-to-end workflow for identifying molecular biomarkers across institutions without data centralization. After federated training, feature attribution methods such as SHAP or integrated gradients are applied locally to identify which genes, proteins, or transcripts drive predictions.

• Each site computes feature importance on its own cohort
• Attribution scores are aggregated via federated averaging to produce a global biomarker ranking
• Enables multi-institutional genome-wide association studies (GWAS) with built-in privacy