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Differential Privacy-enabled Federated Learning for Sensitive Health Data
[article]
2020
arXiv
pre-print
Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different
arXiv:1910.02578v3
fatcat:7okfkjznyvb6fdway4attytbxe