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Distributed Learning with a Local Touch
Improving Efficiency in Multiparty Learning
Shiva
Livre broché | Anglais
27,95 €
+ 55 points
Description
Multiparty learning as an emerging topic, many of the related frameworks and ap-plications are proposed. In this section, we explore the extent of these frameworks and technologies. Yang et al.72 provide a comprehensive survey of existing works on a secure fed-erated learning framework. Bonawitz et al.8 build a scalable production system for Federated Learning in the domain of mobile devices. Konečn`yetal.30 propose ways to reduce communication costs in federated learning. Nishio and Yonetani44 propose a new Federated Learning protocol, FedCS, which can actively manage computing workers based on their resource conditions. Zhao et al.75 notice that conventional federated learning fails on learning non-IID data and propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Smith et al.63 propose fed-erated multi-task learning, which is a novel systems-aware optimization method, MOCHA.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 86
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9783384254221
- Date de parution :
- 08-06-24
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 152 mm x 229 mm
- Poids :
- 136 g
