Description: Distributed Learning with a Local Touch by Shiva 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. Konen`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. FORMAT Paperback CONDITION Brand New Details ISBN3384254228 Author Shiva Pages 86 Publisher Tredition Gmbh Year 2024 ISBN-13 9783384254221 Format Paperback Publication Date 2024-06-08 Imprint Tredition Gmbh Subtitle Improving Efficiency in Multiparty Learning Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:160626253;
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ISBN-13: 9783384254221
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Book Title: Distributed Learning with a Local Touch
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