all AI news
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency. (arXiv:2001.09782v3 [cs.DC] UPDATED)
Jan. 6, 2022, 2:10 a.m. | Tien-Dung Cao, Tram Truong-Huu, Hien Tran, Khanh Tran
cs.LG updates on arXiv.org arxiv.org
Deep learning has achieved great success in many applications. However, its
deployment in practice has been hurdled by two issues: the privacy of data that
has to be aggregated centrally for model training and high communication
overhead due to transmission of a large amount of data usually geographically
distributed. Addressing both issues is challenging and most existing works
could not provide an efficient solution. In this paper, we develop FedPC, a
Federated Deep Learning Framework for Privacy Preservation and Communication …
arxiv communication deep learning deep learning framework framework learning privacy
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
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
Senior AI & Data Engineer
@ Bertelsmann | Kuala Lumpur, 14, MY, 50400
Analytics Engineer
@ Reverse Tech | Philippines - Remote