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

Erscheinungsjahr 2024
ISBN: 978-3-384-25422-1
Verlag: tredition

Buch, Englisch, 84 Seiten, Format (B × H): 155 mm x 234 mm, Gewicht: 157 g

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.

Zielgruppe

Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning" targets researchers and professionals working on machine learning, specifically in distributed learning techniques like federated learning. It assumes understanding of multiparty learning and the challenges of efficiency with local data.

Autoren/Hrsg.

Shiva

Über Autor(innen)

Shiva
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