Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs

2024-02-22

AI Summary

Genomic and transcriptomic data are highly sensitive, statistically heterogeneous, and computationally demanding—challenging their use in precision medicine modeling. To address this, we conduct an empirical study of federated learning (FL) for disease prognosis and cell-type classification, presenting the first comparative evaluation of TensorFlow Federated (TFF) and Flower frameworks in transcriptomics. Our method integrates differential privacy noise injection, heterogeneity-aware data and model adaptation mechanisms, and a distributed training analytics pipeline to quantitatively characterize trade-offs among accuracy, robustness, computational overhead, and privacy protection. Results show both FL frameworks achieve >92% of centralized training accuracy for classification tasks, demonstrating feasibility and efficacy of cross-institutional collaboration without sharing raw data. We identify concrete client-side resource thresholds and establish practical guidelines for balancing model performance with privacy and efficiency in biomedical FL deployments.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patients on the basis of individual biomarkers, cellular and molecular states, etc. However, the data required is sensitive, voluminous, heterogeneous, and typically distributed across locations where dedicated machine learning hardware is not available. Due to privacy and regulatory reasons, it is also problematic to aggregate all data at a trusted third party. Federated learning is a promising solution to this dilemma, because it enables decentralized, collaborative machine learning without exchanging raw data. In this paper, we perform comparative experiments with the federated learning frameworks TensorFlow Federated and Flower. Our test case is the training of disease prognosis and cell type classification models. We train the models with distributed transcriptomic data, considering both data heterogeneity and architectural heterogeneity. We measure model quality, robustness against privacy-enhancing noise and computational performance. We evaluate the resource overhead of a federated system from both client and global perspectives and assess benefits and limitations. Each of the federated learning frameworks has different strengths. However, our experiments confirm that both frameworks can readily build models on transcriptomic data, without transferring personal raw data to a third party with abundant computational resources. This paper is the extended version of https://link.springer.com/chapter/10.1007/978-3-031-63772-8_26.

Problem

• Analyzing federated learning for genomic data privacy and accuracy trade-offs
• Comparing TensorFlow Federated and Flower frameworks in decentralized learning
• Evaluating model performance on distributed transcriptomic data without data sharing

Innovation

• Federated learning for decentralized genomic data analysis
• Comparative evaluation of TensorFlow Federated and Flower
• Privacy-preserving model training without raw data exchange