3 papers
cs.LG2024
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
cs.LG2024
Efficient and Scalable Implementation of Differentially Private Deep Learning without Shortcuts
Sebastian Rodriguez Beltran, Marlon Tobaben, Joonas Jälkö +2
Differentially private stochastic gradient descent (DP-SGD) is the standard algorithm for training machine learning models under differential privacy (DP). The most common DP-SGD p…
cs.LG2023
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Lukas Prediger, Joonas Jälkö, Antti Honkela +1
Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible. We p…