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Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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A tutorial on energy-based learning
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang · 2006
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Regulation eu 2016/679 of the european parliament and of the council of 27 april 2016
G. D. P. Regulation · 2016
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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
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Differentially private federated learning: A client level perspective
R. C. Geyer, T. Klein, and M. Nabi · 2017
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Deep models under the gan: information leakage from collaborative deep learning
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
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Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Earlier work this paper cites.
Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
Earlier work this paper cites.
Protection against reconstruction and its applications in private federated learning
A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers · 2018
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Property inference attacks on fully connected neural networks using permutation invariant representations
K. Ganju, Q. Wang, W. Yang, C. A. Gunter, and N. Borisov · 2018
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Towards demystifying membership inference attacks
S. Truex, L. Liu, M. E. Gursoy, L. Yu, and W. Wei · 2018
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Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
N. Carlini, C. Liu, Ú. Erlingsson, J. Kos, and D. Song · 2019
Cited alongside, same era.
Implicit generation and modeling with energy-based models
Y. Du and I. Mordatch · 2019
Cited alongside, same era.
Evaluating differentially private machine learning in practice
B. Jayaraman and D. Evans · 2019
Cited alongside, same era.
Fedmd: Heterogenous federated learning via model distillation
D. Li and J. Wang · 2019
Cited alongside, same era.
On the convergence of fedavg on non-iid data
Racism and discrimination in covid-19 responses
D. Devakumar, G. Shannon, S. S. Bhopal, and I. Abubakar · 2020
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Fedml: A research library and benchmark for federated machine learning
C. He, S. Li, J. So, X. Zeng, M. Zhang, H. Wang, X. Wang, P. Vepakomma, A. Singh, H. Qiu, et al · 2020
Later among the works it cites.
Ensemble distillation for robust model fusion in federated learning
T. Lin, L. Kong, S. U. Stich, and M. Jaggi · 2020
Later among the works it cites.
Privacy and robustness in federated learning: Attacks and defenses
L. Lyu, H. Yu, X. Ma, L. Sun, J. Zhao, Q. Yang, and P. S. Yu · 2020
Later among the works it cites.
Federated model distillation with noise-free differential privacy
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X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2019
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
M. Nasr, R. Shokri, and A. Houmansadr · 2019
Cited alongside, same era.
A hybrid approach to privacy-preserving federated learning
S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, and Y. Zhou · 2019
Cited alongside, same era.
Beyond inferring class representatives: User-level privacy leakage from federated learning
Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi · 2019
Cited alongside, same era.
Dba: Distributed backdoor attacks against federated learning
C. Xie, K. Huang, P.-Y. Chen, and B. Li · 2019
Cited alongside, same era.
Deep leakage from gradients
L. Zhu, Z. Liu, and S. Han · 2019
Cited alongside, same era.
L. Sun and L. Lyu · 2020
Later among the works it cites.
A collaborative online ai engine for ct-based covid-19 diagnosis
Y. Xu, L. Ma, F. Yang, Y. Chen, K. Ma, J. Yang, X. Yang, Y. Chen, C. Shu, Z. Fan, et al · 2020
Later among the works it cites.
Secure deep graph generation with link differential privacy
C. Yang, H. Wang, K. Zhang, L. Chen, and L. Sun · 2020
Later among the works it cites.
Fedgraphnn: A federated learning benchmark system for graph neural networks
C. He, K. Balasubramanian, E. Ceyani, C. Yang, H. Xie, L. Sun, L. He, L. Yang, S. Y. Philip, Y. Rong, et al · 2021
Closest in time.
Membership inference attacks on machine learning: A survey
H. Hu, Z. Salcic, G. Dobbie, and X. Zhang · 2021
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Global knowledge distillation in federated learning
W. Pan and L. Sun · 2021
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Ldp-fl: Practical private aggregation in federated learning with local differential privacy
L. Sun, J. Qian, X. Chen, and P. S. Yu · 2021
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Membership inference attacks on knowledge graphs
Y. Wang and L. Sun · 2021
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Fedmood: Federated learning on mobile health data for mood detection
X. Xu, H. Peng, L. Sun, M. Z. A. Bhuiyan, L. Liu, and L. He · 2021
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