# A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data

arXiv: https://arxiv.org/abs/2511.06064 · HTML: https://mlsanity.com/paper/2511.06064/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 8 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Scalable Membership Inference Attacks via Quantile Regression](https://mlsanity.com/paper/2307.03694/) (2023)
- [FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System](https://mlsanity.com/paper/2303.10837/) (2023)
- [Label-Only Model Inversion Attacks via Knowledge Transfer](https://mlsanity.com/paper/2310.19342/) (2023)
- [QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning](https://mlsanity.com/paper/2411.05260/) (2024)

## Built on by (most cited first, 2 of 2)

- [Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques](https://mlsanity.com/paper/2603.05158/) (2026)
- [MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics](https://mlsanity.com/paper/2607.25107/) (2026)

## Abstract (arXiv)

Omics data is widely employed in medical research to identify disease mechanisms and contains highly sensitive personal information. Federated Learning (FL) with Differential Privacy (DP) can ensure the protection of omics data privacy against malicious user attacks. However, FL with the DP method faces an inherent trade-off: stronger privacy protection degrades predictive accuracy due to injected noise. On the other hand, Homomorphic Encryption (HE) allows computations on encrypted data and enables aggregation of encrypted gradients without DP-induced noise can increase the predictive accuracy. However, it may increase the computation cost. To improve the predictive accuracy while considering the computational ability of heterogeneous clients, we propose a Privacy-Preserving Machine Learning (PPML)-Hybrid method by introducing HE. In the proposed PPML-Hybrid method, clients distributed select either HE or DP based on their computational resources, so that HE clients contribute noise-free updates while DP clients reduce computational overhead. Meanwhile, clients with high computational resources clients can flexibly adopt HE or DP according to their privacy needs. Performance evaluation on omics datasets show that our proposed method achieves comparable predictive accuracy while significantly reducing computation time relative to HE-only. Additionally, it outperforms DP-only methods under equivalent or stricter privacy budgets.