Fetching the paper…
Reading the bibliography…
In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
Earlier work this paper cites.
Characterizations of an empirical influence function for detecting influential cases in regression
R Dennis Cook and Sanford Weisberg · 1980
Earlier work this paper cites.
Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
Earlier work this paper cites.
On robustness properties of convex risk minimization methods for pattern recognition
Andreas Christmann and Ingo Steinwart · 2004
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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.
Sampling algorithms and coresets for \ \backslash ell_p regression
Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, and Michael W Mahoney · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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.
Human activity recognition with smartphones dataset, 2013
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al · 2013
Earlier work this paper cites.
A public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al · 2013
Earlier work this paper cites.
Safe screening of non-support vectors in pathwise svm computation
Kohei Ogawa, Yoshiki Suzuki, and Ichiro Takeuchi · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Earlier work this paper cites.
Efficient approximation of cross-validation for kernel methods using bouligand influence function
Yong Liu, Shali Jiang, and Shizhong Liao · 2014
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Using deep learning for image-based plant disease detection
Sharada P Mohanty, David P Hughes, and Marcel Salathé · 2016
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
Earlier work this paper cites.
Understanding black-box predictions via influence functions
Pang-Wei Koh and Percy Liang · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et al · 2017
Cited alongside, same era.
Privacy loss in apple’s implementation of differential privacy on macos 10.12
Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, and Xiaofeng Wang · 2017
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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 · 2018
Cited alongside, same era.
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, and Christopher Ré · 2020
Later among the works it cites.
Byzantine-resilient secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr · 2020
Later among the works it cites.
Data-Aware Privacy-Preserving Machine Learning
Aleksei Triastcyn · 2020
Later among the works it cites.
Visual transformers: Token-based image representation and processing for computer vision, 2020
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda · 2020
Later among the works it cites.
Evaluating and rewarding teamwork using cooperative game abstractions
Tom Yan, Christian Kroer, and Alexander Peysakhovich · 2020
Later among the works it cites.
Practical and private (deep) learning without sampling or shuffling
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
Cited alongside, same era.
Generative models for effective ml on private, decentralized datasets
Sean Augenstein, H Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, et al · 2019
Cited alongside, same era.
Federated learning of out-of-vocabulary words
Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Françoise Beaufays · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 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
Cited alongside, same era.
Efficient task-specific data valuation for nearest neighbor algorithms
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas J Spanos, and Dawn Song · 2019
Cited alongside, same era.
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
Later among the works it 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, et al · 2021
Later among the works it cites.
Learning from History for Byzantine Robust Optimization
Sai Praneeth Karimireddy, Lie He, and Martin Jaggi · 2021
Later among the works it cites.
Sample-level data selection for federated learning
Anran Li, Lan Zhang, Juntao Tan, Yaxuan Qin, Junhao Wang, and Xiang-Yang Li · 2021
Later among the works it cites.
Overcoming noisy and irrelevant data in federated learning
Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, and Kin K Leung · 2021
Later among the works it cites.
A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
Later among the works it cites.
Toward understanding the influence of individual clients in federated learning
Yihao Xue, Chaoyue Niu, Zhenzhe Zheng, Shaojie Tang, Chengfei Lyu, Fan Wu, and Guihai Chen · 2021
Later among the works it cites.
Opacus: User-friendly differential privacy library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov · 2021
Later among the works it cites.
Opacus: User-friendly differential privacy library in pytorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov · 2021
Later among the works it cites.
Multi-epoch matrix factorization mechanisms for private machine learning
Christopher A Choquette-Choo, H Brendan McMahan, Keith Rush, and Abhradeep Thakurta · 2022
Closest in time.
A distributed differentially private algorithm for resource allocation in unboundedly large settings
Panayiotis Danassis, Aleksei Triastcyn, and Boi Faltings · 2022
Closest in time.
Unlocking high-accuracy differentially private image classification through scale, 2022
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
Closest in time.
Fedauxfdp: Differentially private one-shot federated distillation, 2022
Haley Hoech, Roman Rischke, Karsten Müller, and Wojciech Samek · 2022
Closest in time.
On privacy and personalization in cross-silo federated learning
Ken Liu, Shengyuan Hu, Steven Z Wu, and Virginia Smith · 2022
Closest in time.
Differentially private shapley values for data evaluation
Lauren Watson, Rayna Andreeva, Hao-Tsung Yang, and Rik Sarkar · 2022
Closest in time.
TCT: Convexifying federated learning using bootstrapped neural tangent kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, and Michael Jordan · 2022
Closest in time.
Dmlr: Data-centric machine learning research–past, present and future
Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, et al · 2023
Closest in time.