Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
Produktnummer:
189cac94fe1edd4922b9879a63f9be401c
| Autor: | Shiva |
|---|---|
| Themengebiete: | Collaborative AI Collaborative Learning Communication Efficiency Decentralized AI Differential Privacy Edge Computing Education Federated Learning Local Adaptation Scalability |
| Veröffentlichungsdatum: | 08.06.2024 |
| EAN: | 9783384254221 |
| Sprache: | Englisch |
| Seitenzahl: | 84 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | tredition |
Produktinformationen "Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning"
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.
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