Fetching the paper…
Reading the bibliography…
Vertical Federated Learning (VFL) refers to the collaborative training of a model on a dataset where the features of the dataset are split among multiple data owners, while label information is owned by a single data owner.
The MNIST database of handwritten digits, 1998
Yann LeCun, Corinna Cortes, and Christopher JC Burges. 1998 · 1998
Earlier work this paper cites.
Differential privacy: A survey of results. In International conference on theory and applications of models of computation . Springer, 1–19
Cynthia Dwork. 2008 · 2008
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016 · 2016
Earlier work this paper cites.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017 · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics . PMLR, 1273–1282
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche. 2017 · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
Cited alongside, same era.
Entity resolution and federated learning get a federated resolution
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2018 · 2018
Cited alongside, same era.
The California Consumer Privacy Act: Towards a European-Style Privacy Regime in the United States
Stuart L Pardau. 2018 · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. 2018 · 2018
Cited alongside, same era.
A communication efficient vertical federated learning framework
Yang Liu, Yan Kang, Liping Li, Xinwei Zhang, Yong Cheng, Tianjian Chen, Mingyi Hong, and Qiang Yang. 2019 · 2019
Later among the works it cites.
Nick Angelou, Ayoub Benaissa, Bogdan Cebere, William Clark, Adam James Hall, Michael A Hoeh, Daniel Liu, Pavlos Papadopoulos, Robin Roehm, Robert Sandmann, et al · 2020
Later among the works it cites.
SplitNN-driven Vertical Partitioning
Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, and Ramesh Raskar. 2020 · 2020
Later among the works it cites.
Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
John Chen, Cameron Wolfe, Zhao Li, and Anastasios Kyrillidis. 2019 · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019 · 2019
Cited alongside, same era.
Chandra Thapa, Mahawaga Arachchige Pathum Chamikara, and Seyit Camtepe. 2020 · 2020
Later among the works it cites.