no code implementations • 10 Oct 2023 • Tatsuki Koga, Kamalika Chaudhuri, David Page
In this work, we take a fresh look at federated learning with a focus on causal inference; specifically, we look at estimating the average treatment effect (ATE), an important task in causal inference for healthcare applications, and provide a federated analytics approach to enable ATE estimation across multiple sites along with differential privacy (DP) guarantees at each site.
no code implementations • 14 Jul 2023 • Tatsuki Koga, Congzheng Song, Martin Pelikan, Mona Chitnis
Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee.
no code implementations • 13 Jan 2022 • Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
When this is the case, we observe that the influence of a single participant (sensitivity) can be reduced by subsampling and/or filtering in time, while still meeting privacy requirements.
1 code implementation • 28 Nov 2018 • Tatsuki Koga, Naoki Nonaka, Jun Sakuma, Jun Seita
Deep learning has significant potential for medical imaging.