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

arXiv: https://arxiv.org/abs/2402.14527 · HTML: https://mlsanity.com/paper/2402.14527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [When the Curious Abandon Honesty: Federated Learning Is Not Private](https://mlsanity.com/paper/2112.02918/) (2021)
- [One-Shot Federated Learning](https://mlsanity.com/paper/1902.11175/) (2019)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)
- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [Federated learning with class imbalance reduction](https://mlsanity.com/paper/2011.11266/) (2020)
- [Differential Privacy-enabled Federated Learning for Sensitive Health Data](https://mlsanity.com/paper/1910.02578/) (2019)
- [Differentially Private Federated Learning for Cancer Prediction](https://mlsanity.com/paper/2101.02997/) (2021)
- [DeepGene Transformer: Transformer for the gene expression-based classification of cancer subtypes](https://mlsanity.com/paper/2108.11833/) (2021)
- [A Privacy-Preserving Federated Learning Approach for Kernel methods](https://mlsanity.com/paper/2306.02677/) (2023)

## Built on by (most cited first, 1 of 1)

- [Securing Genomic Data Against Inference Attacks in Federated Learning Environments](https://mlsanity.com/paper/2505.07188/) (2025)

## Abstract (arXiv)

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.