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September 10, 2025Open Access

Federated Learning for Privacy-Preserving Medical Diagnosis on Edge Devices: A Comprehensive Research Framework

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Authors

VCVignan Chintala TDTapan K DattaNirma University

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Implication

This framework enables privacy-preserving medical diagnosis using federated learning on edge devices, suggesting a transformative approach for healthcare data sharing.

Key Points

Federated learning models achieve up to 98% accuracy in medical imaging diagnostics while ensuring data privacy.
The proposed system enables healthcare institutions to collaboratively train models without sharing sensitive patient data.
Advanced privacy techniques like homomorphic encryption and differential privacy ensure compliance with healthcare regulations.
The framework reduces communication overhead by 25-60% compared to traditional medical AI approaches.

Cite This Study

Chintala et al. (2025) studied this question.

synapsesocial.com/papers/68c1c31b54b1d3bfb60f0829 https://doi.org/10.36227/techrxiv.175459854.42540575/v1

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Implication

This framework enables privacy-preserving medical diagnosis using federated learning on edge devices, suggesting a transformative approach for healthcare data sharing.

Federated Learning for Privacy-Preserving Medical Diagnosis on Edge Devices: A Comprehensive Research Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

Federated Learning for Privacy-Preserving Medical Diagnosis on Edge Devices: A Comprehensive Research Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

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