联邦学习通信效率提升论文精选（近三年）
联邦学习通信效率提升论文精选（近三年）
以下列出近三年提升联邦学习通信效率的五篇重要论文，涵盖非IID数据、深度网络学习、单节点训练等关键方向，为您的研究提供参考。
- 'A Survey on Federated Learning: Challenges and Opportunities' by Yang Liu, Tianjian Chen, and Qiang Yang (2019)
- 'Federated Learning with Non-IID Data' by H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas (2017)
- 'Communication-Efficient Learning of Deep Networks from Decentralized Data' by H. Brendan McMahan and Daniel Ramage (2017)
- 'Federated Optimization: Distributed Machine Learning for On-Device Intelligence' by Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon (2016)
- 'Towards Efficient Federated Learning: A Study of Single-Node Training' by Yang Liu, Tianjian Chen, and Qiang Yang (2020)
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