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Vous voulez être sûr que vos cadeaux seront sous le sapin de Noël à temps? Nos magasins vous accueillent à bras ouverts. La plupart de nos magasins sont ouverts également les dimanches, vous pouvez vérifier les heures d'ouvertures sur notre site.
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- 7.000.0000 titres dans notre catalogue
- Payer en toute sécurité
- Toujours un magasin près de chez vous
Advances and Open Problems in Federated Learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawit, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G L D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X Yu, Han Yu, Sen Zhao
99,45 €
+ 198 points
Description
The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more. This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems. Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.
Spécifications
Parties prenantes
- Auteur(s) :
Peter KairouzH Brendan McMahanBrendan AventAurélien BelletMehdi BennisArjun Nitin BhagojiKallista BonawitZachary CharlesGraham CormodeRachel CummingsRafael G L D'OliveiraHubert EichnerSalim El RouayhebDavid EvansJosh GardnerZachary GarrettAdrià GascónBadih GhaziPhillip B GibbonsMarco GruteserZaid HarchaouiChaoyang HeLie HeZhouyuan HuoBen HutchinsonJustin HsuMartin JaggiTara JavidiGauri JoshiMikhail KhodakJakub KonecnýAleksandra KorolovaFarinaz KoushanfarSanmi KoyejoTancrède LepointYang LiuPrateek MittalMehryar MohriRichard NockAyfer ÖzgürRasmus PaghHang QiDaniel RamageRamesh RaskarMariana RaykovaDawn SongWeikang SongSebastian U StichZiteng SunAnanda Theertha SureshFlorian TramèrPraneeth VepakommaJianyu WangLi XiongZheng XuQiang YangFelix X YuHan YuSen Zhao
- Editeur:
Contenu
- Nombre de pages :
- 224
- Langue:
- Anglais
- Collection :
Caractéristiques
- EAN:
- 9781680837889
- Date de parution :
- 23-06-21
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 156 mm x 234 mm
- Poids :
- 322 g
