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September 29, 2025Internet of Things and Cloud ComputingOpen Access

A Privacy-Preserving Data Governance in Cross-Border Telemedicine Using Federated Learning and Differential Privacy in Kenya

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Authors

MMMichael MeyoCICynthia IkamariAMAnthony Mile

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Overview

This study demonstrates improved accuracy with federated learning in telemedicine while ensuring patient data privacy, suggesting a new governance model for hospitals across borders.

Key Points

  • Federated learning enhances model accuracy, improving recall and F1 scores while keeping patient data localized.
  • Adding differential privacy significantly reduces the success of model-inversion attacks by about 8.4 percent.
  • Comparative analysis used synthetic EHR data from seven hospitals across Kenya, Tanzania, and Uganda.
  • The approach incorporates an attack simulator and an ε register, allowing hospitals to manage privacy during cross-border care.

Cite This Study

Meyo et al. (2025) studied this question.

synapsesocial.com/papers/68da58e0c1728099cfd11723https://doi.org/10.11648/j.iotcc.20251303.12
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Secure Federated Learning for High-Dimensional Healthcare Data Using Differential Privacy2025
  2. 2A Privacy-Preserving Federated Learning Framework for Multi-Institutional Healthcare AI with Differential Privacy, Byzantine Fault Tolerance, and Gradient Inversion Defence2026
  3. 3Data Privacy in Federated Learning: The Trade-Offs in Balancing Operational Performance with Confidentiality2026
  4. 4Federated Learning in Healthcare: Balancing Data Privacy and Predictive Accuracy in Multi-Institutional Settings2023 · 2 citations
  5. 5Advanced Privacy-Preserving AI Techniques for Distributed Disease Diagnosis2026