October 4, 2024INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

Federated Learning in Healthcare: A Path Towards Decentralized and Secure Medical Insights

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Decentralized machine learning through federated learning enables multi-site clinical data analysis without requiring central aggregation of sensitive medical records.
Synthesis of technical architectures examines differential privacy and secure multiparty computation to preserve patient data security while maintaining model generalizability.
Application supports scalable disease prediction and personalized medicine, yet practical implementation faces communication overhead and regulatory compliance barriers.

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Implication

Review reveals federated learning enables decentralized machine learning across sensitive clinical datasets, suggesting a viable path toward secure collaborative intelligence in healthcare.

Cite This Study

Mishra et al. (2024) studied this question.

synapsesocial.com/papers/68e55c90e2b3180350efa42a https://doi.org/10.55041/ijsrem37791

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Implication

Review reveals federated learning enables decentralized machine learning across sensitive clinical datasets, suggesting a viable path toward secure collaborative intelligence in healthcare.

Federated Learning in Healthcare: A Path Towards Decentralized and Secure Medical Insights

Key Points

Discussion

Implication

Cite This Study

Federated Learning in Healthcare: A Path Towards Decentralized and Secure Medical Insights

Key Points

Discussion

Implication

Cite This Study

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