Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
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(Sprache: Englisch)
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...
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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.
Autoren-Porträt von Shiva
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Bibliographische Angaben
- Autor: Shiva
- 2024, 84 Seiten, Masse: 15,5 x 23,4 cm, Kartoniert (TB), Englisch
- Verlag: tredition
- ISBN-10: 3384254228
- ISBN-13: 9783384254221
Sprache:
Englisch
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