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Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
Induction of decision trees
J. Ross Quinlan · 1986
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Health insurance portability and accountability act of 1996
Accountability Act · 1996
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A generalisation, a simplification, and some applications of paillier’s probabilistic public-key system, presented at the 4th international workshop on practice and theory in public key cryptosystems, cheju island
I Damgard and M Jurik · 2001
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Practical privacy: the sulq framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Scaling distributed machine learning with the parameter server
Mu Li, David G Andersen, Jun Woo Park, Alexander J Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J Shekita, and Bor-Yiing Su · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Mllib: Machine learning in apache spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, et al · 2016
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Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46
General Data Protection Regulation · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, Rachid Guerraoui, Julien Stainer, et al · 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 y Arcas · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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https://www.businessinsider.com/macys-bloomingdales-hack-disclosed-2018-7
Macy’s is warning customers that their information might have been stolen in a data breach, July 2018 · 2018
Cited alongside, same era.
Google exposed user data, feared repercussions of disclosing to public, Oct. 2018
The Wall Street Journal · 2018
Cited alongside, same era.
Yahoo to pay $50 million, offer credit monitoring for massive security breach, Oct. 2018
NBC News · 2018
Cited alongside, same era.
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
Cited alongside, same era.
Facebook security breach exposes accounts of 50 million users, September 2018
Braintorrent: A peer-to-peer environment for decentralized federated learning, 2019
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, and Christian Wachinger · 2019
Later among the works it cites.
Towards federated graph learning for collaborative financial crimes detection
Toyotaro Suzumura, Yi Zhou, Natahalie Barcardo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Daniel Klyashtorny, Heiko Ludwig, et al · 2019
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Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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Hybridalpha: An efficient approach for privacy-preserving federated learning
Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig · 2019
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Statistical model aggregation via parameter matching
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, and Nghia Hoang · 2019
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New York Times · 2018
Cited alongside, same era.
A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, and Rui Zhang · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Towards taming the resource and data heterogeneity in federated learning
Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, and Yue Cheng · 2019
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
Later among the works it cites.
https://flask.palletsprojects.com/
Flask, (Accessed July, 01, 2020) · 2020
Closest in time.
https://grpc.io/
grpc, (Accessed July, 01, 2020) · 2020
Closest in time.
https://github.com/FederatedAI/FATE
FATE Github, (Accessed July, 01, 2020) · 2020
Closest in time.
Tifl: A tier-based federated learning system
Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng · 2020
Closest in time.
Coordinate-wise median: Not bad, not bad, pretty good
Sumit Goel and Wade Hann-Caruthers · 2020
Closest in time.