自适应裁剪梯度联邦学习框架相关论文推荐

本文推荐了十篇关于自适应裁剪梯度联邦学习框架的论文，涵盖了联邦学习的挑战、方法、未来方向以及系统设计等方面，并提供了相关论文链接。

'Adaptive Federated Optimization' by Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Łukasz Kaiser, Heinrich K. M. K. andrews, Abhishek Katti, Aleksandra Korolova, Farinaz Koushanfar, Sewoong Oh, Rasmus Pagh, Mariana Raykova, Sameer Singh, Karn Seth, Mahdi Soltanolkotabi, Jingwei Xu, Ziteng Sun, and Ananda Theertha Suresh. https://arxiv.org/abs/2003.00295
'Communication-Efficient Learning of Deep Networks from Decentralized Data' by H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. https://arxiv.org/abs/1602.05629
'Federated Learning: Challenges, Methods, and Future Directions' by Yang Liu, Tian Li, Yongqiang Lyu, and Dusit Niyato. https://arxiv.org/abs/1907.09693
'Communication-Efficient Distributed Optimization using an Approximate Newton-type Method' by Nicolas Loizou and Marios Polycarpou. https://arxiv.org/abs/1905.13174
'Federated Learning with Non-IID Data' by Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Łukasz Kaiser, Heinrich K. M. K. andrews, Abhishek Katti, Aleksandra Korolova, Farinaz Koushanfar, Sewoong Oh, Rasmus Pagh, Mariana Raykova, Sameer Singh, Karn Seth, Mahdi Soltanolkotabi, Jingwei Xu, Ziteng Sun, and Ananda Theertha Suresh. https://arxiv.org/abs/1910.00189
'Federated Learning: A Survey on Challenges and Opportunities' by Chaoyang He, Wei Wu, Jinyang Li, and Gaogang Xie. https://arxiv.org/abs/1908.07873
'Local SGD Converges Fast and Communicates Little' by Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. https://arxiv.org/abs/1805.09767
'Machine Learning with Privacy-Preserving Distributed Data: A Survey' by Jakub Konečný, Brendan McMahan, and Daniel Ramage. https://arxiv.org/abs/1812.00984
'Towards Federated Learning at Scale: System Design' by H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. https://arxiv.org/abs/1902.01046
'Towards Federated Learning with Byzantine Robustness' by Peter Kairouz, Karan Singh, and Prateek Mittal. https://arxiv.org/abs/1908.07839