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

(Autor)

Buch | Softcover
84 Seiten
2024
tredition (Verlag)
978-3-384-25422-1 (ISBN)
28,10 inkl. MwSt
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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Erscheint lt. Verlag 8.6.2024
Verlagsort nathan park
Sprache englisch
Maße 155 x 234 mm
Gewicht 155 g
Themenwelt Sozialwissenschaften Pädagogik Bildungstheorie
Schlagworte Collaborative AI • Collaborative Learning • Communication Efficiency • decentralized ai • differential privacy • edge computing • Education • federated learning • Local adaptation • Scalability
ISBN-10 3-384-25422-8 / 3384254228
ISBN-13 978-3-384-25422-1 / 9783384254221
Zustand Neuware
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