all AI news
Federated Learning on Riemannian Manifolds with Differential Privacy
April 17, 2024, 4:42 a.m. | Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra
cs.LG updates on arXiv.org arxiv.org
Abstract: In recent years, federated learning (FL) has emerged as a prominent paradigm in distributed machine learning. Despite the partial safeguarding of agents' information within FL systems, a malicious adversary can potentially infer sensitive information through various means. In this paper, we propose a generic private FL framework defined on Riemannian manifolds (PriRFed) based on the differential privacy (DP) technique. We analyze the privacy guarantee while establishing the convergence properties. To the best of our knowledge, …
abstract agents arxiv cs.cr cs.lg differential differential privacy distributed federated learning framework information machine machine learning math.oc paper paradigm privacy systems through type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Robotics Technician - 3rd Shift
@ GXO Logistics | Perris, CA, US, 92571