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dc.contributor.authorWang, Jianyu
dc.contributor.authorCharles, Zachary
dc.contributor.authorXu, Zheng
dc.contributor.authorJoshi, Gauri
dc.contributor.authorMcMahan, H. Brendan
dc.contributor.authorArcas, Blaise Aguera y
dc.contributor.authorAl-Shedivat, Maruan
dc.contributor.authorAndrew, Galen
dc.contributor.authorAvestimehr, Salman
dc.contributor.authorDaly, Katharine
dc.contributor.authorData, Deepesh
dc.contributor.authorDiggavi, Suhas
dc.contributor.authorEichner, Hubert
dc.contributor.authorGadhikar, Advait
dc.contributor.authorGarrett, Zachary
dc.contributor.authorGirgis, Antonious M.
dc.contributor.authorHanzely, Filip
dc.contributor.authorHard, Andrew
dc.contributor.authorHe, Chaoyang
dc.contributor.authorHorvath, Samuel
dc.contributor.authorHuo, Zhouyuan
dc.contributor.authorIngerman, Alex
dc.contributor.authorJaggi, Martin
dc.contributor.authorJavidi, Tara
dc.contributor.authorKairouz, Peter
dc.contributor.authorKale, Satyen
dc.contributor.authorKarimireddy, Sai Praneeth
dc.contributor.authorKonecny, Jakub
dc.contributor.authorKoyejo, Sanmi
dc.contributor.authorLi, Tian
dc.contributor.authorLiu, Luyang
dc.contributor.authorMohri, Mehryar
dc.contributor.authorQi, Hang
dc.contributor.authorReddi, Sashank J.
dc.contributor.authorRichtarik, Peter
dc.contributor.authorSinghal, Karan
dc.contributor.authorSmith, Virginia
dc.contributor.authorSoltanolkotabi, Mahdi
dc.contributor.authorSong, Weikang
dc.contributor.authorSuresh, Ananda Theertha
dc.contributor.authorStich, Sebastian U.
dc.contributor.authorTalwalkar, Ameet
dc.contributor.authorWang, Hongyi
dc.contributor.authorWoodworth, Blake
dc.contributor.authorWu, Shanshan
dc.contributor.authorYu, Felix X.
dc.contributor.authorYuan, Honglin
dc.contributor.authorZaheer, Manzil
dc.contributor.authorZhang, Mi
dc.contributor.authorZhang, Tong
dc.contributor.authorZheng, Chunxiang
dc.contributor.authorZhu, Chen
dc.contributor.authorZhu, Wennan
dc.date.accessioned2021-07-28T11:51:04Z
dc.date.available2021-07-28T11:51:04Z
dc.date.issued2021-07-14
dc.identifier.urihttp://hdl.handle.net/10754/670335
dc.description.abstractFederated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.
dc.description.sponsorshipThe authors thank the early feedback and review by Sean Augenstein, Kallista Bonawitz, Corinna Cortes, and Keith Rush. This work is supported by GCP credits provided by Google Cloud. The simulation experiments are implemented with the TensorFlow Federated package. This paper originated at the discussion session moderated by Zheng Xu and Gauri Joshi at the Workshop on Federated Learning and Analytics, virtually held June 29–30th, 2020. During the discussion, a general consensus about the need for a guide about federated optimization is reached.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2107.06917.pdf
dc.rightsArchived with thanks to arXiv
dc.titleA Field Guide to Federated Optimization
dc.typePreprint
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.eprint.versionPre-print
dc.contributor.institutionCarnegie Mellon University
dc.contributor.institutionGoogle Research
dc.contributor.institutionUniversity of Southern California
dc.contributor.institutionUniversity of California, Los Angeles
dc.contributor.institutionToyota Technological Institute at Chicago
dc.contributor.institutionEcole Polytechnique F´ed´erale de Lausanne
dc.contributor.institutionUniversity of California, San Diego
dc.contributor.institutionUniversity of Illinois Urbana-Champaign
dc.contributor.institutionUniversity of Wisconsin–Madison
dc.contributor.institutionStanford University
dc.contributor.institutionMichigan State Univeristy
dc.contributor.institutionThe Hong Kong University of Science and Technology
dc.contributor.institutionUniversity of Maryland, College Park
dc.identifier.arxivid2107.06917
kaust.personHorvath, Samuel
kaust.personRichtarik, Peter
refterms.dateFOA2021-07-28T11:51:50Z


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