A Privacy-Preserving Federated Learning Method for Heterogeneous Clients

Yusaku Negoya; Feifei Cui; Zilong Zhang; Miao Pan; Tomoaki Ohtsuki; Aohan Li

cs.CRNov 8, 2025

A Privacy-Preserving Federated Learning Method for Heterogeneous Clients

Authors: Yusaku Negoya, Feifei Cui, Zilong Zhang, Miao Pan, Tomoaki Ohtsuki, Aohan Li

Organizations: Department of Computer and Network Engineering, The University of Electro-Communications, Tokyo, Japan · School of Computer Science and Technology, Hainan University, Haikou 570228, China. · Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204, USA · Department of Information and Computer Science, Keio University, Yokohama, Japan

Abstract

Federated Learning (FL) with Differential Privacy (DP) can ensure the protection of healthcare 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) enables computations on encrypted data and the aggregation of encrypted gradients without introducing DP-induced noise, thereby potentially increasing 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. In the proposed PPML-Hybrid method, clients distributedly 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 can flexibly adopt HE or DP according to their privacy needs. Performance evaluation on omics datasets shows 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.

Figures & tables

Fig. 1: The proposed hybrid architecture with HE and DP clients.

Parameter	Value
Dataset and Model
Dataset	Human Breast Cancer (23 patients / 68 sections / 30,612 spots)
Target Genes	250 most highly expressed genes
Data Partitioning	3 sections/client
Model Architecture	ST-Net
FL Hyperparameters

TABLE I: Experimental Parameter Settings

Fig. 2: Loss (MSE) vs. Number of Clients.

Fig. 3: Comparison of PPML-Hybrid and PPML-Omics at different privacy budgets ( $\epsilon=4,8$ ) with fixed HE ratio $\alpha=0.5$ .

Privacy-Preserving Federated Learning

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Apr 30, 2026J/K move · Enter open · S save

Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling

Gaurang Sharma, Juha Pajula, Aada Illikainen, Markus Rautell+3

VTT Technical Research Centre of Finland Ltd, 33720, Tampere, Finland · VTT Technical Research Centre of Finland Ltd, 02150, Espoo, Finland · Solita Oy, 00100 Helsinki, Finland+1

Homomorphic Encryption Privacy-Preserving ML Differential Privacy Cardiovascular Risk Prediction Federated Learning

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Protecting sensitive health data while enabling collaborative analysis is a central challenge in healthcare. Traditional machine learning (ML) requires institutions to pool anonymized patient records, centralizing analytical development and privacy risks at a single site. Privacy-enhancing technologies (PETs), including Differential Privacy (DP) and Homomorphic Encryption (HE), can mitigate these risks. However, they are mainly studied in conventional data-sharing settings and often introduce trade-offs, including reduced model utility, higher computational cost, and increased implementation complexity. Federated Learning (FL) reduces data centralization by enabling institutions to train models locally and share only model updates. Nevertheless, FL does not eliminate privacy risks, as shared parameters or gradients may still reveal sensitive information. Integrating DP or HE into FL can strengthen privacy guarantees, yet their comparative performance and deployment implications in real-world healthcare settings remain unclear. We systematically evaluated DP and HE integration in FL under real-world conditions, comparing them with standard FL and centralized ML (cML) to quantify privacy-utility trade-offs in multi-institutional settings. Using nationwide Swedish healthcare data, we evaluated cardiovascular disease risk prediction using logistic regression (LR) and neural network (NN) learners. FL with HE achieved performance comparable to cML but introduced measurable cryptographic overhead, particularly in the NN implementation. FL with DP incurred lower computational cost; however, LR was more sensitive to calibrated noise than the NN, resulting in greater performance degradation. Our findings provide practical guidance for deploying privacy-preserving FL in fragmented healthcare systems.

Abstract

Privacy-Preserving Federated Learning

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