A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations
Published in Journal of Machine Learning for Biomedical Imaging, 2022
This paper is an extension of the work presented at the Information Processing in Medical Imaging 2021 conference: we introduce formal differential privacy guarantees compatibly with the proposed federated Bayesian framework and Expectation Maximization optimization scheme.
Recommended citation: Balelli I., Silva S., Lorenzi M. (2022). A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. Journal of Machine Learning for Biomedical Imaging. IPMI 2021 special issue, vol. 1, pp 1-36.