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Federated learning has attracted attention in recent years for collaboratively training data on distributed devices with privacy-preservation.
Fair end-to-end window-based congestion control
Jeonghoon Mo and Jean Walrand · 2000
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera Arcas · 2017
Earlier work this paper cites.
Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar · 2018
Earlier work this paper cites.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Earlier work this paper cites.
EU Personal Data Protection in Policy and Practice
Bart Custers, Alan M Sears, Francien Dechesne, Ilina Georgieva, Tommaso Tani, and Simone Van der Hof · 2019
Earlier work this paper cites.
Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2019
Earlier work this paper cites.
Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Client selection for federated learning with heterogeneous resources in mobile edge
Takayuki Nishio and Ryo Yonetani · 2019
Cited alongside, same era.
Rethinking transport layer design for distributed machine learning
Jiacheng Xia, Gaoxiong Zeng, Junxue Zhang, Weiyan Wang, Wei Bai, Junchen Jiang, and Kai Chen · 2019
Cited alongside, same era.
Fedhealth: A federated transfer learning framework for wearable healthcare
Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao · 2020
Cited alongside, same era.
Measuring Broadband America Mobile Data
Federal Communications Commission · 2020
Cited alongside, same era.
Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 2020
Cited alongside, same era.
Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Later among the works it cites.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Later among the works it cites.
Federated learning in mobile edge networks: A comprehensive survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 2020
Later among the works it cites.
Federated learning with communication delay in edge networks
Frank Po-Chen Lin, Christopher G Brinton, and Nicolo Michelusi · 2020
Later among the works it cites.
Collaborative fairness in federated learning
Lingjuan Lyu, Xinyi Xu, Qian Wang, and Han Yu · 2020
Later among the works it cites.
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Cdc: Classification driven compression for bandwidth efficient edge-cloud collaborative deep learning
Yuanrui Dong, Peng Zhao, Hanqiao Yu, Cong Zhao, and Shusen Yang · 2020
Cited alongside, same era.
Achieving outcome fairness in machine learning models for social decision problems
Boli Fang, Miao Jiang, Pei-yi Cheng, Jerry Shen, and Yi Fang
Cited in the paper.
California consumer privacy act
CCPA · 2021
Closest in time.
Openmined
Openmined · 2021
Closest in time.