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Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets.
S. a. health insurance portability and accountability act
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Distributed learning, communication complexity and privacy
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Trading computation for communication: Distributed stochastic dual coordinate ascent
T. Yang · 2013
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Y. Zhang, J. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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The algorithmic foundations of differential privacy
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Fast distributed coordinate descent for non-strongly convex losses
O. Fercoq, Z. Qu, P. Richtárik, and M. Takáč · 2014
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Distributed stochastic optimization and learning
O. Shamir and N. Srebro · 2014
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Ethereum: A secure decentralised generalised transaction ledger
G. Wood et al · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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The composition theorem for differential privacy
P. Kairouz, S. Oh, and P. Viswanath · 2015
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Federated optimization: Distributed optimization beyond the datacenter
J. Konečnỳ, B. McMahan, and D. Ramage · 2015
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Adding vs. averaging in distributed primal-dual optimization
C. Ma, V. Smith, M. Jaggi, M. I. Jordan, P. Richtárik, and M. Takáč · 2015
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Deep learning with differential privacy
Abadi and et al · 2016
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Multi-variable agents decomposition for DCOPs
F. Fioretto, W. Yeoh, and E. Pontelli · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2016
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Fast and differentially private algorithms for decentralized collaborative machine learning
A. Bellet, R. Guerraoui, M. Taziki, and M. Tommasi · 2017
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Differentially private federated learning: A client level perspective, 2017
R. C. Geyer, T. Klein, and M. Nabi · 2017
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Rényi differential privacy
Improving the privacy and accuracy of admm-based distributed algorithms, 2018
X. Zhang, M. M. Khalili, and M. Liu · 2018
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Communication-efficient distributed optimization of self-concordant empirical loss
Y. Zhang and L. Xiao · 2018
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Towards decentralized deep learning with differential privacy, 06 2019
H.-P. Cheng, P. Yu, H. Hu, S. Zawad, F. Yan, S. Li, and Y. Chen · 2019
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Local model poisoning attacks to byzantine-robust federated learning, 2019
M. Fang, X. Cao, J. Jia, and N. Z. Gong · 2019
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Privacy-preserving federated data sharing
F. Fioretto and P. Van Hentenryck · 2019
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Rényi differential privacy of the sampled gaussian mechanism, 2019
I. Mironov, K. Talwar, and L. Zhang · 2019
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I. Mironov · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Dynamic differential privacy for admm-based distributed classification learning
T. Zhang and Q. Zhu · 2017
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Personalized and private peer-to-peer machine learning, 2018
A. Bellet, R. Guerraoui, M. Taziki, and M. Tommasi · 2018
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Leasgd: an efficient and privacy-preserving decentralized algorithm for distributed learning, 2018
H.-P. Cheng, P. Yu, H. Hu, F. Yan, S. Li, H. Li, and Y. Chen · 2018
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Distributed constraint optimization problems and applications: A survey
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Identifying medical diagnoses and treatable diseases by image-based deep learning
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Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive
J. Weng, J. Weng, J. Zhang, M. Li, Y. Zhang, and W. Luo · 2019
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Distributed alternating direction method of multipliers for linearly constrained optimization over a network
R. Carli and M. Dotoli · 2020
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Can ai help in screening viral and covid-19 pneumonia?
M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. A. Emadi, and et al · 2020
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Covid-19 image data collection: Prospective predictions are the future
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Covid-19 image data collection
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Inverting gradients – how easy is it to break privacy in federated learning?, 2020
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller · 2020
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A privacy-preserving and accountable multi-agent learning framework
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