Web: http://arxiv.org/abs/2202.08922

Sept. 22, 2022, 1:12 a.m. | Hyunsung Cho, Akhil Mathur, Fahim Kawsar

cs.LG updates on arXiv.org arxiv.org

Federated Learning (FL) enables distributed training of machine learning
models while keeping personal data on user devices private. While we witness
increasing applications of FL in the area of mobile sensing, such as human
activity recognition (HAR), FL has not been studied in the context of a
multi-device environment (MDE), wherein each user owns multiple data-producing
devices. With the proliferation of mobile and wearable devices, MDEs are
increasingly becoming popular in ubicomp settings, therefore necessitating the
study of FL in …

arxiv environments 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