Shiva: Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning, Kartoniert / Broschiert

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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. 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.