Navigating the Privacy-Accuracy Tradeoff: Federated Survival Analysis with Binning and Differential Privacy
Varsha Gouthamchand,
Johan van Soest,
Giovanni Arcuri
et al.
Abstract:Federated learning (FL) offers a decentralized approach to model training, allowing for data-driven insights while safeguarding patient privacy across institutions. In the Personal Health Train (PHT) paradigm, it is local model gradients from each institution, aggregated over a sample size of its own patients that are transmitted to a central server to be globally merged, rather than transmitting the patient data itself. However, certain attacks on a PHT infrastructure may risk compromising sensitive data. Thi… Show more
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