GlueFL : reconciling client sampling and model masking for bandwidth efficient federated learning
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: He, Shiqi
issued: 2023-01-18T17:16:14Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Federated learning (FL) is an effective technique to directly involve edge devices in machine learning (ML) training while preserving client privacy. However, the substantial communication overhead of FL makes training challenging when edge devices have limited network bandwidth. Existing work to optimize FL bandwidth overlooks downstream transmission and does not account for FL client sampling. We propose GlueFL, a framework that incorporates new client sampling and model compression algorithms to mitigate low download bandwidths of FL clients. GlueFL prioritizes recently used clients and bounds the number of changed positions in compression masks in each round. We analyse FL convergence under GlueFL’s sticky sampling, and show that our proposed weighted aggregation preserves unbiasedness of updates and convergence. We evaluate GlueFL empirically, and demonstrate downstream bandwidth and training time savings on three public datasets. On average, our evaluation shows that GlueFL spends 29% less training time with a 27% less downstream bandwidth overhead as compared to three state-of-the-art strategies.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83666
note: GlueFL: Reconciling Client Samplingand Model Masking for BandwidthEfficient Federated LearningbyShiqi HeB.Sc., Hong Kong Polytechnic University, 2020A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)January 2023© Shiqi He 2023The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:GlueFL: Reconciling Client Sampling andModel Masking for Band-width Efficient Federated Learningsubmitted by Shiqi He in partial fulfillment of the requirements for thedegree of Master of Science in Computer Science.Examining Committee:Ivan Beschastnikh, Associate Professor, Computer Science, UBCSupervisorMathias Lécuyer, Assistant Professor, Computer Science, UBCSupervisory Committee MemberiiAbstractFederated learning (FL) is an effective technique to directly involve edgedevices in machine learning (ML) training while preserving client privacy.However, the substantial communication overhead of FL makes training chal-lenging when edge devices have limited network bandwidth. Existing workto optimize FL bandwidth overlooks downstream transmission and does notaccount for FL client sampling.We propose GlueFL, a framework that incorporates new client samplingand model compression algorithms to mitigate low download bandwidths ofFL clients. GlueFL prioritizes recently used clients and bounds the numberof changed positions in compression masks in each round.We analyse FL convergence under GlueFL’s sticky sampling, and showthat our proposed weighted aggregation preserves unbiasedness of updatesand convergence.We evaluate GlueFL empirically, and demonstrate downstream band-width and training time savings on three public datasets. On average, ourevaluation shows that GlueFL spends 29% less training time with a 27%less downstream bandwidth overhea…
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0423118
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Beschastnikh, Ivan
type: Text
identifier URI: http://hdl.handle.net/2429/83666