Web: http://arxiv.org/abs/2206.07902

June 17, 2022, 1:10 a.m. | Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

cs.LG updates on arXiv.org arxiv.org

While the application of differential privacy (DP) has been well-studied in
cross-device federated learning (FL), there is a lack of work considering DP
for cross-silo FL, a setting characterized by a limited number of clients each
containing many data subjects. In cross-silo FL, usual notions of client-level
privacy are less suitable as real-world privacy regulations typically concern
in-silo data subjects rather than the silos themselves. In this work, we
instead consider the more realistic notion of silo-specific item-level privacy,
where …

arxiv cross federated learning learning lg on personalization privacy

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY