Fetching the paper…
Reading the bibliography…
A central tenet of Federated learning (FL), which trains models without centralizing user data, is privacy.
Towards Federated Learning at Scale: System Design
Kallista Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 1902
Earlier work this paper cites.
Federated Learning for Emoji Prediction in a Mobile Keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 1906
Earlier work this paper cites.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 1910
Earlier work this paper cites.
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 1912
Earlier work this paper cites.
Matching pursuits with time-frequency dictionaries
S.G. Mallat and Zhifeng Zhang · 1941
Earlier work this paper cites.
A shortest augmenting path algorithm for dense and sparse linear assignment problems
R. Jonker and A. Volgenant · 1987
Earlier work this paper cites.
Constrained k-means clustering
Paul S. Bradley, Kristin P. Bennett, and Ayhan Demiriz · 2000
Earlier work this paper cites.
Bleu: A Method for Automatic Evaluation of Machine Translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Residual Energy-Based Models for Text Generation
Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, and Marc’Aurelio Ranzato · 2004
Earlier work this paper cites.
ROUGE: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit
Ron Rubinstein, Michael Zibulevsky, and Michael Elad · 2008
Earlier work this paper cites.
What Can We Learn Privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Earlier work this paper cites.
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, and Kunal Talwar · 2012
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2013
Earlier work this paper cites.
On implementing 2D rectangular assignment algorithms
David F. Crouse · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Pointer Sentinel Mixture Models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Earlier work this paper cites.
Practical Secure Aggregation for Privacy Preserving Machine Learning
Kallista 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.
Federated Learning: Collaborative Machine Learning without Centralized Training Data, April 2017
Brendan McMahan and Daniel Ramage · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
Earlier work this paper cites.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai · 2017
Cited alongside, same era.
Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Algorithms that remember: Model inversion attacks and data protection law
Michael Veale, Reuben Binns, and Lilian Edwards · 2018
Cited alongside, same era.
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2018
Cited alongside, same era.
TAG: Gradient Attack on Transformer-based Language Models
Jieren Deng, Yijue Wang, Ji Li, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, and Caiwen Ding · 2021
Later among the works it cites.
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, and Tom Goldstein · 2021
Later among the works it cites.
Introducing Android’s Private Compute Services, September 2021
Suzanne Frey · 2021
Later among the works it cites.
Predicting Text Selections with Federated Learning, November 2021
Florian Hartmann · 2021
Later among the works it cites.
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, and Robert Sim · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Protection Against Reconstruction and Its Applications in Private Federated Learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2019
Cited alongside, same era.
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Federated Learning, May 2019
Team Google Research · 2019
Cited alongside, same era.
Federated Learning for Mobile Keyboard Prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2019
Cited alongside, same era.
Evaluating Differentially Private Machine Learning in Practice
Bargav Jayaraman and David Evans · 2019
Cited alongside, same era.
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, Sudeep Agarwal, Julien Freudiger, Andrew Byde, Abhishek Bhowmick, Gaurav Kapoor, Si Beaumont, Áine Cahill, Dominic Hughes, Omid Javidbakht, Fei Dong, Rehan Rishi, and Stanley Hung · 2021
Later among the works it cites.
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
Later among the works it cites.
Privacy preservation in federated learning: An insightful survey from the GDPR perspective
Nguyen Truong, Kai Sun, Siyao Wang, Florian Guitton, and YiKe Guo · 2021
Later among the works it cites.
Tight Lower Bounds for Locally Differentially Private Selection
Jonathan Ullman · 2021
Later among the works it cites.
User Label Leakage from Gradients in Federated Learning
Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, and Max Mühlhäuser · 2021
Later among the works it cites.
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, and Wennan Zhu · 2021
Later among the works it cites.
See Through Gradients: Image Batch Recovery via GradInversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M. Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
Later among the works it cites.
Quantifying Memorization Across Neural Language Models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2022
Closest in time.
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal Diaz, Andre Manoel, and Robert Sim · 2022
Closest in time.
LAMP: Extracting Text from Gradients with Language Model Priors
Dimitar I. Dimitrov, Mislav Balunović, Nikola Jovanović, and Martin Vechev · 2022
Closest in time.
Recovering Private Text in Federated Learning of Language Models
Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, and Danqi Chen · 2022
Closest in time.
Large Language Models Can Be Strong Differentially Private Learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2022
Closest in time.
Differentially Private Federated Learning on Heterogeneous Data
Maxence Noble, Aurélien Bellet, and Aymeric Dieuleveut · 2022
Closest in time.
ONNX Operator Schemas
Documentation ONNX · 2022
Closest in time.