Shiva: Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
Buch
- tredition, 06/2024
- Einband: Kartoniert / Broschiert, Paperback
- Sprache: Englisch
- ISBN-13: 9783384254221
- Bestellnummer: 11892056
- Umfang: 84 Seiten
- Gewicht: 157 g
- Maße: 234 x 155 mm
- Stärke: 7 mm
- Erscheinungstermin: 8.6.2024
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
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. Kone n`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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