Fetching the paper…
Reading the bibliography…
In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices.
A Generalized Probability Density Function for Double-Bounded Random Processes
Ponnambalam Kumaraswamy · 1980
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Gradient-based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Infinite Latent Feature Models and the Indian Buffet Process
Zoubin Ghahramani and Thomas L Griffiths · 2006
Earlier work this paper cites.
Hierarchical Beta Processes and the Indian Buffet Process
Romain Thibaux and Michael I Jordan · 2007
Earlier work this paper cites.
ImageNet: A Large-scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Large Scale Distributed Deep Networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
Earlier work this paper cites.
Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Efficient Private Statistics with Succinct Sketches
Luca Melis, George Danezis, and Emiliano De Cristofaro · 2015
Cited alongside, same era.
Deep Learning with Differential Privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2017
Cited alongside, same era.
Communication-efficient Learning of Deep Networks from Decentralized Data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 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.
Federated Multi-task Learning
Variational Federated Multi-Task Learning
Luca Corinzia and Joachim M Buhmann · 2019
Later among the works it cites.
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 2019
Later among the works it cites.
Adaptive Gradient-based Meta-learning Methods
Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar · 2019
Later among the works it cites.
FedMD: Heterogenous Federated Learning via Model Distillation
Daliang Li and Junpu Wang · 2019
Later among the works it cites.
Federated Learning: Challenges, Methods, and Future Directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
Cited alongside, same era.
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Cited alongside, same era.
Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
Cited alongside, same era.
Automatic Threat Recognition of Prohibited Items at Aviation Checkpoints with X-ray Imaging: A Deep Learning Approach
Kevin J Liang, Geert Heilmann, Christopher Gregory, Souleymane O. Diallo, David Carlson, Gregory P. Spell, John B. Sigman, Kris Roe, and Lawrence Carin · 2018
Cited alongside, same era.
Detecting and Classifying Lesions in Mammograms with Deep Learning
Dezsö Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner, and István Csabai · 2018
Cited alongside, same era.
Federated Learning with Non-IID Data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Cited alongside, same era.
Later among the works it cites.
Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Later among the works it cites.
Private Federated Learning with Domain Adaptation
Daniel Peterson, Pallika Kanani, and Virendra J Marathe · 2019
Later among the works it cites.
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.
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Later among the works it cites.
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, and Virginia Smith · 2020
Closest in time.
Bayesian Nonparametric Weight Factorization for Continual Learning
Nikhil Mehta, Kevin J Liang, and Lawrence Carin · 2020
Closest in time.
Federated Learning with Matched Averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
Closest in time.