Fetching the paper…
Reading the bibliography…
Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past.
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.
Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger and Lawrence K Saul · 2009
Earlier work this paper cites.
Differential privacy in new settings
Cynthia Dwork · 2010
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
On using very large target vocabulary for neural machine translation
Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Personnet: Person re-identification with deep convolutional neural networks
Lin Wu, Chunhua Shen, and Anton van den Hengel · 2016
Earlier work this paper cites.
Learning with privacy at scale
Apple Privacy Team · 2017
Earlier work this paper cites.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 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.
Rényi differential privacy
Ilya Mironov · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
The complexity of differential privacy
Salil Vadhan · 2017
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Earlier work this paper cites.
Group normalization
Yuxin Wu and Kaiming He · 2018
Earlier work this paper cites.
Bounding user contributions: A bias-variance trade-off in differential privacy
Kareem Amin, Alex Kulesza, Andres Munoz, and Sergei Vassilvtiskii · 2019
Earlier work this paper cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Cited alongside, same era.
Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Are all layers created equal?, 2019
Chiyuan Zhang, Samy Bengio, and Yoram Singer · 2019
Cited alongside, same era.
The discrete gaussian for differential privacy
Clément L Canonne, Gautam Kamath, and Thomas Steinke · 2020
Cited alongside, same era.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
Later among the works it cites.
Hyperparameter tuning with renyi differential privacy
Nicolas Papernot and Thomas Steinke · 2021
Later among the works it cites.
Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
Later among the works it cites.
Efficient and private federated learning with partially trainable networks
Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, and Mingqing Chen · 2021
Later among the works it cites.
Federated reconstruction: Partially local federated learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Google’s differential privacy libraries., 2020
DP Team · 2020
Cited alongside, same era.
Smoothly bounding user contributions in differential privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Federated visual classification with real-world data distribution
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Tight approximate differential privacy for discrete-valued mechanisms using fft, 2020
Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela · 2020
Cited alongside, same era.
Later among the works it cites.
Disclosure avoidance for the 2020 census: An introduction, 2021
US Census Bureau · 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, et al · 2021
Later among the works it cites.
Sphereface2: Binary classification is all you need for deep face recognition
Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, and Rita Singh · 2021
Later among the works it cites.
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al · 2021
Later among the works it cites.
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Later among the works it cites.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
Closest in time.
Fine-tuning with differential privacy necessitates an additional hyperparameter search
Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, and Abhradeep Thakurta · 2022
Closest in time.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
Closest in time.
Spherefed: Hyperspherical federated learning
Xin Dong, Sai Qian Zhang, Ang Li, and HT Kung · 2022
Closest in time.
Connect the dots: Tighter discrete approximations of privacy loss distributions
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi · 2022
Closest in time.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Closest in time.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
Closest in time.
Toward training at imagenet scale with differential privacy
Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta · 2022
Closest in time.
Federated learning with formal differential privacy guarantees, 2022
Brendan McMahan and Abhradeep Thakurta · 2022
Closest in time.
Improving federated learning face recognition via privacy-agnostic clusters
Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu, and Yuanqing Lin · 2022
Closest in time.
Efficient image representation learning with federated sampled softmax
Sagar M Waghmare, Hang Qi, Huizhong Chen, Mikhail Sirotenko, and Tomer Meron · 2022
Closest in time.
On the unreasonable effectiveness of federated averaging with heterogeneous data
Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, and Tong Zhang · 2022
Closest in time.
Digiface-1m: 1 million digital face images for face recognition
Gwangbin Bae, Martin de La Gorce, Tadas Baltrušaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, and Jingjing Shen · 2023
Closest in time.
How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta · 2023
Closest in time.
Share your representation only: Guaranteed improvement of the privacy-utility tradeoff in federated learning
Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, and Reza Shokri · 2023
Closest in time.