Web: http://arxiv.org/abs/2207.04330

Sept. 22, 2022, 1:12 a.m. | Neelkamal Bhuyan, Sharayu Moharir, Gauri Joshi

cs.LG updates on arXiv.org arxiv.org

Federated Learning (FL) is a variant of distributed learning where edge
devices collaborate to learn a model without sharing their data with the
central server or each other. We refer to the process of training multiple
independent models simultaneously in a federated setting using a common pool of
clients as multi-model FL. In this work, we propose two variants of the popular
FedAvg algorithm for multi-model FL, with provable convergence guarantees. We
further show that for the same amount of …

arxiv federated learning

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

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France