{"product_id":"distributed-learning-with-a-local-touch-improving-efficiency-in-multiparty-learning-von-shiva","title":"Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning","description":"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.\nYang 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. Konečn`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.\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783384254221\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9783384254221","offer_id":48872001962309,"sku":"9783384254221","price":28.1,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/94b78308-e2ab-4347-9d32-1a1ee910bf85.jpg?v=1776487330","url":"https:\/\/shop.autorenwelt.de\/products\/distributed-learning-with-a-local-touch-improving-efficiency-in-multiparty-learning-von-shiva","provider":"Autorenwelt Shop","version":"1.0","type":"link"}