34 citations · 58 across the 3 of their papers we have counts for
3 papers
Differentially private cross-silo federated learning
Mikko A. Heikkilä, Antti Koskela, Kana Shimizu +2
Strict privacy is of paramount importance in distributed machine learning. Federated learning, with the main idea of communicating only what is needed for learning, has been recent…
Differentially Private Bayesian Learning on Distributed Data
Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski +3
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become esta…
Amino acid composition and thermal stability of protein structures: the free energy geography of the Protein Data Bank
Antonio Deiana, Kana Shimizu, Andrea Giansanti
We study the combined influence of amino acid composition and chain length on the thermal stability of protein structures. A new parameterization of the internal free energy is con…