Fetching the paper…
Reading the bibliography…
Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates.
The weak byzantine generals problem
L. Lamport · 1983
Earlier work this paper cites.
Casting out demons: Sanitizing training data for anomaly sensors
G. F. Cretu, A. Stavrou, M. E. Locasto, S. J. Stolfo, and A. D. Keromytis · 2008
Earlier work this paper cites.
Differential privacy: A survey of results
C. Dwork · 2008
Earlier work this paper cites.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller · 2008
Earlier work this paper cites.
Exploiting machine learning to subvert your spam filter
B. Nelson, M. Barreno, F. J. Chi, A. D. Joseph, B. I. Rubinstein, U. Saini, C. A. Sutton, J. D. Tygar, and K. Xia · 2008
Earlier work this paper cites.
Twitter sentiment classification using distant supervision
A. Go, R. Bhayani, and L. Huang · 2009
Earlier work this paper cites.
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
F. D. McSherry · 2009
Earlier work this paper cites.
Learning in a large function space: Privacy-preserving mechanisms for SVM learning
B. I. Rubinstein, P. L. Bartlett, L. Huang, and N. Taft · 2009
Earlier work this paper cites.
Adversarial machine learning
L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. D. Tygar · 2011
Earlier work this paper cites.
Sharing graphs using differentially private graph models
A. Sala, X. Zhao, C. Wilson, H. Zheng, and B. Y. Zhao · 2011
Earlier work this paper cites.
Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and P. Laskov · 2012
Earlier work this paper cites.
Near-optimal differentially private principal components
K. Chaudhuri, A. Sarwate, and K. Sinha · 2012
Earlier work this paper cites.
Differentially private online learning
P. Jain, P. Kothari, and A. Thakurta · 2012
Earlier work this paper cites.
Functional mechanism: regression analysis under differential privacy
J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett · 2012
Earlier work this paper cites.
Local privacy, data processing inequalities, and statistical minimax rates
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
Earlier work this paper cites.
Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart · 2014
Earlier work this paper cites.
Differential privacy and machine learning: a survey and review
Z. Ji, Z. C. Lipton, and C. Elkan · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
Earlier work this paper cites.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Earlier work this paper cites.
Data Poisoning Attacks against Autoregressive Models
S. Alfeld, X. Zhu, and P. Barford · 2016
Earlier work this paper cites.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Tying word vectors and word classifiers: A loss framework for language modeling
H. Inan, K. Khosravi, and R. Socher · 2016
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
Earlier work this paper cites.
Data poisoning attacks on factorization-based collaborative filtering
B. Li, Y. Wang, A. Singh, and Y. Vorobeychik · 2016
Earlier work this paper cites.
Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar · 2016
Earlier work this paper cites.
Using the output embedding to improve language models
O. Press and L. Wolf · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction APIs
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
P. Blanchard, R. Guerraoui, J. Stainer, et al · 2017
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
Earlier work this paper cites.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Y. Chen, L. Su, and J. Xu · 2017
Earlier work this paper cites.
EMNIST: Extending MNIST to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik · 2017
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
R. C. Geyer, T. Klein, and M. Nabi · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
Earlier work this paper cites.
Minimax filter: learning to preserve privacy from inference attacks
J. Hamm · 2017
Earlier work this paper cites.
S. Hardy, W. Henecka, H. Ivey-Law, R. Nock, G. Patrini, G. Smith, and B. Thorne · 2017
Earlier work this paper cites.
Deep models under the GAN: information leakage from collaborative deep learning
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
Earlier work this paper cites.
Developing and validating a survival prediction model for NSCLC patients through distributed learning across 3 countries
A. Jochems, T. M. Deist, I. El Naqa, M. Kessler, C. Mayo, J. Reeves, S. Jolly, M. Matuszak, R. Ten Haken, J. van Soest, et al · 2017
Earlier work this paper cites.
Trojaning attack on neural networks
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
Cited alongside, same era.
SecureML: A system for scalable privacy-preserving machine learning
P. Mohassel and Y. Zhang · 2017
Cited alongside, same era.
Practical Black-Box Attacks against Machine Learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
Cited alongside, same era.
Automatic Differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Memguard: Defending against black-box membership inference attacks via adversarial examples
J. Jia, A. Salem, M. Backes, Y. Zhang, and N. Z. Gong · 2019
Later among the works it cites.
Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2019
Later among the works it cites.
Data poisoning against differentially-private learners: Attacks and defenses
Y. Ma, X. Zhu, and J. Hsu · 2019
Later among the works it cites.
Towards adversarial malware detection: lessons learned from pdf-based attacks
D. Maiorca, B. Biggio, and G. Giacinto · 2019
Later among the works it cites.
Exploiting unintended feature leakage in collaborative learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
Cited alongside, same era.
Certified defenses for data poisoning attacks
J. Steinhardt, P. W. W. Koh, and P. S. Liang · 2017
Cited alongside, same era.
Protection against reconstruction and its applications in private federated learning
A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers · 2018
Cited alongside, same era.
Federated learning of predictive models from federated electronic health records
T. S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. C. Paschalidis, and W. Shi · 2018
Cited alongside, same era.
The secret sharer: Measuring unintended neural network memorization & extracting secrets
N. Carlini, C. Liu, J. Kos, Ú. Erlingsson, and D. Song · 2018
Cited alongside, same era.
Draco: Byzantine-resilient distributed training via redundant gradients
L. Chen, H. Wang, Z. Charles, and D. Papailiopoulos · 2018
Cited alongside, same era.
Comprehensive privacy analysis of deep learning
M. Nasr, R. Shokri, and A. Houmansadr · 2019
Later among the works it cites.
Knockoff nets: Stealing functionality of black-box models
T. Orekondy, B. Schiele, and M. Fritz · 2019
Later among the works it cites.
Robust aggregation for federated learning
K. Pillutla, S. M. Kakade, and Z. Harchaoui · 2019
Later among the works it cites.
DETOX: A redundancy-based framework for faster and more robust gradient aggregation
S. Rajput, H. Wang, Z. Charles, and D. Papailiopoulos · 2019
Later among the works it cites.
Federated Learning for Emoji Prediction in a Mobile Keyboard
S. Ramaswamy, R. Mathews, K. Rao, and F. Beaufays · 2019
Later among the works it cites.
Can you really backdoor federated learning?
Z. Sun, P. Kairouz, A. T. Suresh, and H. B. McMahan · 2019
Later among the works it cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 2019
Later among the works it cites.
Latent backdoor attacks on deep neural networks
Y. Yao, H. Li, H. Zheng, and B. Y. Zhao · 2019
Later among the works it cites.
How to backdoor federated learning
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov · 2020
Closest in time.
Gan-leaks: A taxonomy of membership inference attacks against generative models
D. Chen, N. Yu, Y. Zhang, and M. Fritz · 2020
Closest in time.
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies
D. Enthoven and Z. Al-Ars · 2020
Closest in time.
Local model poisoning attacks to Byzantine-robust federated learning
M. Fang, X. Cao, J. Jia, and N. Gong · 2020
Closest in time.
Influence function based data poisoning attacks to top-n recommender systems
M. Fang, N. Z. Gong, and J. Liu · 2020
Closest in time.
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
S. Hong, V. Chandrasekaran, Y. Kaya, T. Dumitraş, and N. Papernot · 2020
Closest in time.
High accuracy and high fidelity extraction of neural networks
M. Jagielski, N. Carlini, D. Berthelot, A. Kurakin, and N. Papernot · 2020
Closest in time.
Auditing Differentially Private Machine Learning: How Private is Private SGD?
M. Jagielski, J. Ullman, and A. Oprea · 2020
Closest in time.
Stolen memories: Leveraging model memorization for calibrated white-box membership inference
K. Leino and M. Fredrikson · 2020
Closest in time.
Learning to detect malicious clients for robust federated learning
S. Li, Y. Cheng, W. Wang, Y. Liu, and T. Chen · 2020
Closest in time.
Threats to federated learning: A survey
L. Lyu, H. Yu, and Q. Yang · 2020
Closest in time.
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
E. Quiring and K. Rieck · 2020
Closest in time.
Certified robustness to label-flipping attacks via randomized smoothing
E. Rosenfeld, E. Winston, P. Ravikumar, and Z. Kolter · 2020
Closest in time.
Exploring backdoor poisoning attacks against malware classifiers
G. Severi, J. Meyer, S. Coull, and A. Oprea · 2020
Closest in time.
Ldp-fed: federated learning with local differential privacy
S. Truex, L. Liu, K.-H. Chow, M. E. Gursoy, and W. Wei · 2020
Closest in time.
On certifying robustness against backdoor attacks via randomized smoothing
B. Wang, X. Cao, N. Z. Gong, et al · 2020
Closest in time.
RAB: Provable Robustness Against Backdoor Attacks
M. Weber, X. Xu, B. Karlas, C. Zhang, and B. Li · 2020
Closest in time.
Backdoor attacks on facial recognition in the physical world
E. Wenger, J. Passananti, Y. Yao, H. Zheng, and B. Y. Zhao · 2020
Closest in time.
Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning
S. Yeom, I. Giacomelli, A. Menaged, M. Fredrikson, and S. Jha · 2020
Closest in time.
B. Zhang, R. Yu, H. Sun, Y. Li, J. Xu, and H. Wang · 2020
Closest in time.
Manipulation attacks in local differential privacy
A. Cheu, A. Smith, and J. Ullman · 2021
Closest in time.
Subpopulation data poisoning attacks
M. Jagielski, G. Severi, N. Pousette Harger, and A. Oprea · 2021
Closest in time.
Hidden backdoors in human-centric language models
S. Li, H. Liu, T. Dong, B. Z. H. Zhao, M. Xue, H. Zhu, and J. Lu · 2021
Closest in time.
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning
V. Shejwalkar and A. Houmansadr · 2021
Closest in time.
Model-targeted poisoning attacks with provable convergence
F. Suya, S. Mahloujifar, A. Suri, D. Evans, and Y. Tian · 2021
Closest in time.
A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola · 2021
Closest in time.
Leakage of dataset properties in multi-party machine learning
W. Zhang, S. Tople, and O. Ohrimenko · 2021
Closest in time.