Web: http://arxiv.org/abs/2201.11311

Jan. 28, 2022, 2:11 a.m. | Hajar Moudoud, Soumaya Cherkaoui, Lyes Khoukhi

cs.LG updates on arXiv.org arxiv.org

Federated learning (FL) is a distributed machine learning (ML) technique that
enables collaborative training in which devices perform learning using a local
dataset while preserving their privacy. This technique ensures privacy,
communication efficiency, and resource conservation. Despite these advantages,
FL still suffers from several challenges related to reliability (i.e.,
unreliable participating devices in training), tractability (i.e., a large
number of trained models), and anonymity. To address these issues, we propose a
secure and trustworthy blockchain framework (SRB-FL) tailored to FL, …

arxiv blockchain federated learning learning

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

Data Scientist

@ Fluent, LLC | Boca Raton, Florida, United States

Big Data ETL Engineer

@ Binance.US | Vancouver

Data Scientist / Data Engineer

@ Kin + Carta | Chicago

Data Engineer

@ Craft | Warsaw, Masovian Voivodeship, Poland

Senior Manager, Data Analytics Audit

@ Affirm | Remote US

Data Scientist - Nationwide Opportunities, AWS Professional Services

@ Amazon.com | US, NC, Virtual Location - N Carolina