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Federated learning suffers from several privacy-related issues that expose the participants to various threats.
In praise of hierarchy
Elliott Jaques · 1991
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Characterizing the internet hierarchy from multiple vantage points
Lakshminarayanan Subramanian, Sharad Agarwal, Jennifer Rexford, and Randy H Katz · 2002
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Generating trusted graphs for trust evaluation in online social networks
Wenjun Jiang, Guojun Wang, and Jie Wu · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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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
Earlier work this paper cites.
Auror: Defending against poisoning attacks in collaborative deep learning systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
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Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Ulfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Earlier work this paper cites.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
Earlier work this paper cites.
Learning differentially private language models without losing accuracy
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Bulletproofs: Short proofs for confidential transactions and more
Benedikt Bünz, Jonathan Bootle, Dan Boneh, Andrew Poelstra, Pieter Wuille, and Greg Maxwell · 2018
Cited alongside, same era.
Draco: Byzantine-resilient distributed training via redundant gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos · 2018
Cited alongside, same era.
Data encoding for byzantine-resilient distributed optimization
Deepesh Data, Linqi Song, and Suhas Diggavi · 2019
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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
Later among the works it cites.
Client-edge-cloud hierarchical federated learning
L Liu, J Zhang, S Song, and KB Letaief · 2019
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Edge-assisted hierarchical federated learning with non-iid data
Lumin Liu, Jun Zhang, SH Song, and Khaled B Letaief · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Fog computing: Current research and future challenges
Julien Gedeon, Jens Heuschkel, Lin Wang, and Max Mühlhäuser · 2018
Cited alongside, same era.
Don’t use large mini-batches, use local sgd
Tao Lin, Sebastian U Stich, Kumar Kshitij Patel, and Martin Jaggi · 2018
Cited alongside, same era.
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
D ^ 2 \hat{}2 : Decentralized training over decentralized data
Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, and Ji Liu · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett · 2018
Cited alongside, same era.
Robust Secure Aggregation for Privacy-Preserving Federated Learning with Adversaries
Lukas Burhhalter · 2019
Cited alongside, same era.
Later among the works it cites.
Efficient privacy-preserving recommendations based on social graphs
Aidmar Wainakh, Tim Grube, Jörg Daubert, and Max Mühlhäuser · 2019
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Tweet beyond the Cage: A Hybrid Solution for the Privacy Dilemma in Online Social Networks
Aidmar Wainakh, Tim Grube, and M Max · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2019
Later among the works it cites.
Verifynet: Secure and verifiable federated learning
Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin · 2019
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Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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World’s Biggest Data Breaches & Hacks
David McCandless · 2020
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