Federated Learning for Cross-Institutional Genomic Data Analysis in Rare Disease Prediction

Abstract

Rare diseases, individually uncommon but collectively impactful, pose substantial challenges for genomic research due to limited patient data availability. Centralized machine learning approaches are often infeasible because of privacy concerns, heterogeneity of datasets, and regulatory restrictions. Federated learning (FL), a decentralized machine learning paradigm, enables collaborative model training across multiple institutions without sharing raw patient data, preserving privacy while enhancing predictive accuracy. This paper provides a comprehensive framework for FL in rare disease genomics. We discuss preprocessing strategies, model architectures, optimization algorithms, privacy-preserving mechanisms, interpretability approaches, and evaluation metrics. We also explore ethical, regulatory, and practical considerations, including fairness, consent, and scalability. Case studies demonstrate the application of FL in oncology and neurology, highlighting its potential to accelerate precision medicine while ensuring health equity and data privacy.