Citation

Hasan, Md. Rokibul and Ahmed, Md. Istiaq and Saha, Sudip and Ishika, Tashnim Khan and Shoaib, Hashibul Ahsan and Hossen, Md. Jakir (2026) Multi-modal federated learning with differential privacy for privacy-preserving healthcare AI. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

The growing adoption of artificial intelligence in healthcare highlights the need for models that can leverage heterogeneous patient data while preserving strict privacy requirements. This paper proposes a novel multi-modal federated learning framework with differential privacy for decentralized healthcare AI. The model integrates electronic health records and ECG time-series using modalityspecific encoders and a shared latent fusion network, enabling comprehensive representation learning without centralizing sensitive data. Differential privacy is incorporated into local updates to provide formal guarantees against information leakage in federated aggregation. Extensive experiments on real-world healthcare datasets show that the proposed method achieves 94.12% accuracy, 93.64% precision, 93.21% recall, 93.42% F1-score, and 95.03% AUC, outperforming centralized, singlemodality, and non-private baselines. The framework also converges 32.4% faster than single-modality federated learning, reaching 90% accuracy in 35 rounds. An ablation study confirms the contribution of multi-modal fusion and class balancing, while client variance analysis shows the lowest performance deviation (±1.2%) under heterogeneous distributions. These results indicate that combining federated optimization, differential privacy, and multi-modal learning provides an effective framework for privacy-preserving clinical AI, with potential for deployment in distributed healthcare settings.

Item Type:	Article
Uncontrolled Keywords:	Federated learning, Multi-modal learning
Subjects:	L Education > LB Theory and practice of education > LB1060 Learning
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 04:15
Last Modified:	04 Aug 2026 04:15
URII:	http://shdl.mmu.edu.my/id/eprint/16481

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