all InfoSec news
Exact Penalty Method for Federated Learning. (arXiv:2208.11231v1 [cs.LG])
Aug. 25, 2022, 1:20 a.m. | Shenglong Zhou, and Geoffrey Ye Li
cs.CR updates on arXiv.org arxiv.org
Federated learning has burgeoned recently in machine learning, giving rise to
a variety of research topics. Popular optimization algorithms are based on the
frameworks of the (stochastic) gradient descent methods or the alternating
direction method of multipliers. In this paper, we deploy an exact penalty
method to deal with federated learning and propose an algorithm, FedEPM, that
enables to tackle four critical issues in federated learning: communication
efficiency, computational complexity, stragglers' effect, and data privacy.
Moreover, it is proven to …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Social Engineer For Reverse Engineering Exploit Study
@ Independent study | Remote
Premium Hub - CoE: Business Process Senior Consultant, SAP Security Role and Authorisations & GRC
@ SAP | Dublin 24, IE, D24WA02
Product Security Response Engineer
@ Intel | CRI - Belen, Heredia
Application Security Architect
@ Uni Systems | Brussels, Brussels, Belgium
Sr Product Security Engineer
@ ServiceNow | Hyderabad, India
Analyst, Cybersecurity & Technology (Initial Application Deadline May 20th, Final Deadline May 31st)
@ FiscalNote | United Kingdom (UK)