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Vertical Federated Learning (vFL) allows multiple parties that own different attributes (e.g.
S. Yang, B. Ren, X. Zhou, and L. Liu · 1911
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New directions in cryptography
W. Diffie and M. Hellman · 1976
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Asymmetrical vertical federated learning
Y. Liu, X. Zhang, and L. Wang · 2004
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Privacy-preserving set operations
L. Kissner and D. Song · 2005
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Privacy-preserving set union
K. Frikken · 2007
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Practical private set intersection protocols with linear complexity
E. De Cristofaro and G. Tsudik · 2010
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Linear-complexity private set intersection protocols secure in malicious model
E. De Cristofaro, J. Kim, and G. Tsudik · 2010
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Private set intersection: Are garbled circuits better than custom protocols?
Y. Huang, D. Evans, and J. Katz · 2012
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Simple and scalable response prediction for display advertising
O. Chapelle, E. Manavoglu, and R. Rosales · 2015
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Private and oblivious set and multiset operations
M. Blanton and E. Aguiar · 2016
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Wide & deep learning for recommender systems
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, et al · 2016
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Efficient batched oblivious prf with applications to private set intersection
V. Kolesnikov, R. Kumaresan, M. Rosulek, and N. Trieu · 2016
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An efficient toolkit for computing private set operations
A. Davidson and C. Cid · 2017
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Scalable multi-party private set-intersection
C. Hazay and M. Venkitasubramaniam · 2017
Cited alongside, same era.
How to simulate it–a tutorial on the simulation proof technique
Y. Lindell · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents
O. Gupta and R. Raskar · 2018
Cited alongside, same era.
Scalable private set intersection based on ot extension
B. Pinkas, T. Schneider, and M. Zohner · 2018
Cited alongside, same era.
Private matching for compute
P. Buddhavarapu, A. Knox, P. Mohassel, S. Sengupta, E. Taubeneck, and V. Vlaskin · 2020
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Splitnn-driven vertical partitioning
I. Ceballos, V. Sharma, E. Mugica, A. Singh, A. Roman, P. Vepakomma, and R. Raskar · 2020
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Vafl: a method of vertical asynchronous federated learning
T. Chen, X. Jin, Y. Sun, and W. Yin · 2020
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Inverting gradients - how easy is it to break privacy in federated learning?
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller · 2020
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An efficient framework for clustered federated learning
A. Ghosh, J. Chung, D. Yin, and K. Ramchandran · 2020
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Spectral signatures in backdoor attacks
B. Tran, J. Li, and A. Madry · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar · 2018
Cited alongside, same era.
Scalable private set union from symmetric-key techniques
V. Kolesnikov, M. Rosulek, N. Trieu, and X. Wang · 2019
Cited alongside, same era.
Measuring calibration in deep learning
J. Nixon, M. Dusenberry, L. Zhang, G. Jerfel, and D. Tran · 2019
Cited alongside, same era.
Spot-light: Lightweight private set intersection from sparse ot extension
B. Pinkas, M. Rosulek, N. Trieu, and A. Yanai · 2019
Cited alongside, same era.
Can we use split learning on 1d cnn models for privacy preserving training?
S. Abuadbba, K. Kim, M. Kim, C. Thapa, S. A. Camtepe, Y. Gao, H. Kim, and S. Nepal · 2020
Cited alongside, same era.
FedBoost: A communication-efficient algorithm for federated learning
J. Hamer, M. Mohri, and A. T. Suresh · 2020
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Lower bounds and optimal algorithms for personalized federated learning
F. Hanzely, S. Hanzely, S. Horváth, and P. Richtarik · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh · 2020
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Acceleration for compressed gradient descent in distributed and federated optimization
Z. Li, D. Kovalev, X. Qian, and P. Richtarik · 2020
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Privacy preserving vertical federated learning for tree-based models
Y. Wu, S. Cai, X. Xiao, G. Chen, and B. C. Ooi · 2020
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Federated accelerated stochastic gradient descent
H. Yuan and T. Ma · 2020
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Label leakage and protection in two-party split learning
O. Li, J. Sun, X. Yang, W. Gao, H. Zhang, J. Xie, V. Smith, and C. Wang · 2021
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