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Könyv Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning SHIVA

Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning

DE

Szerző: SHIVA
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: tredition
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
7 366 Ft
Multiparty learning as an emerging topic, many of the related frameworks and ap-plications are propo...

Információk a könyvről

Szerző
SHIVA
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2024
oldal
84
EAN
9783384254221
Enbook ID
46097882
Kiadó
tredition
Súly
155
Méretek
155 x 6 x 234

Teljes leírás

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. Konecn`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.

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