Understand
Federated learning enables training on a massive number of edge devices.
- To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm.
- We prove that the proposed approach has near-linear convergence to a global optimum, for both strongly convex and a restricted family of non-convex problems.
- Empirical results show that the proposed algorithm converges quickly and tolerates staleness in various applications.
Built on
Health insurance portability and accountability act of 1996
Steve Anderson: HealthInsurance.org · 1996
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Learning multiple layers of features from tiny images
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Slow learners are fast
Martin Zinkevich, John Langford, and Alex J Smola · 2009
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More effective distributed ml via a stale synchronous parallel parameter server
Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B Gibbons, Garth A Gibson, Greg Ganger, and Eric P Xing · 2013
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konevcnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Communication efficient distributed machine learning with the parameter server
Mu Li, David G Andersen, Alexander J Smola, and Kai Yu
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Then
Asynchronous stochastic gradient descent with delay compensation
Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, and Tie-Yan Liu · 2017
Later among the works it cites.
European Union’s General Data Protection Regulation (GDPR)
EU · 2018
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Closest in time.
Family Educational Rights and Privacy Act (FERPA)
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