Ryan Panwar

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2021-204

August 18, 2021

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-204.pdf

Privacy and communication cost concerns have led to interest in federated learning (FL) for edge machine learning applications. While standard machine learning has a rich ecosystem of distributed training libraries, federated learning’s novelty means researchers lack the necessary frameworks to efficiently explore the large design space unique to FL. In this work we propose, build, and evaluate a benchmarking framework for FL algorithms. Our system, RayLEAF, allows users to train FL algorithms in parallel while testing model compression approaches in a simulated federated setting.

Advisors: John DeNero

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BibTeX citation:

@mastersthesis{Panwar:EECS-2021-204,
    Author= {Panwar, Ryan},
    Title= {RayLEAF: Benchmarking Compressed Federated Models},
    School= {EECS Department, University of California, Berkeley},
    Year= {2021},
    Month= {Aug},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-204.html},
    Number= {UCB/EECS-2021-204},
    Abstract= {Privacy and communication cost concerns have led to interest in federated learning (FL) for edge machine learning applications. While standard machine learning has a rich ecosystem of distributed training libraries, federated learning’s novelty means researchers lack the necessary frameworks to efficiently explore the large design space unique to FL. In this work we propose, build, and evaluate a benchmarking framework for FL algorithms. Our system, RayLEAF, allows users to train FL algorithms in parallel while testing model compression approaches in a simulated federated setting.},
}

EndNote citation:

%0 Thesis
%A Panwar, Ryan 
%T RayLEAF: Benchmarking Compressed Federated Models
%I EECS Department, University of California, Berkeley
%D 2021
%8 August 18
%@ UCB/EECS-2021-204
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-204.html
%F Panwar:EECS-2021-204