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Více informací o knize Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning

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Anotace knihy

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.

Parametry knihy

• Plný název: Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
• Podnázev: DE
• Autor: SHIVA
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 84
• EAN: 9783384254221
• ID: 46097882
• Nakladatelství: tredition
• Hmotnost: 155 g
• Rozměry: 6 × 155 × 234 mm
• Rok vydání: 2024

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