Fetching the paper…
Reading the bibliography…
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear.
W. Diffie and M. Hellman, “New directions in cryptography,” IEEE transactions on Information Theory , vol. 22, no. 6, pp. 644–654, 1976
1976
Earlier work this paper cites.
A. Shamir, “How to share a secret,” Communications of the ACM , vol. 22, no. 11, pp. 612–613, 1979
1979
Earlier work this paper cites.
A. C. Yao, “Protocols for secure computations,” in IEEE Symp. on Foundations of Computer Science , 1982, pp. 160–164
1982
Earlier work this paper cites.
W. Hoeffding, “Probability inequalities for sums of bounded random variables,” in The collected works of Wassily Hoeffding . Springer, 1994, pp. 409–426
1994
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of Cryptography Conference . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) . USA: Wiley-Interscience, 2006
2006
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
M. Pathak, S. Rane, and B. Raj, “Multiparty differential privacy via aggregation of locally trained classifiers,” in Advances in Neural Inf. Processing Systems , 2010, pp. 1876–1884
2010
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, “MNIST handwritten digit database,” http://yann. lecun. com/exdb/mnist , 2010
2010
Earlier work this paper cites.
A. Rajkumar and S. Agarwal, “A differentially private stochastic gradient descent algorithm for multiparty classification,” in Int. Conf. on Artificial Intelligence and Statistics (AISTATS’12) , vol. 22, La Palma, Canary Islands, Apr 2012, pp. 933–941
2012
Earlier work this paper cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security , 2015, pp. 1322–1333
2015
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in ACM SIGSAC Conference on Computer and Communications Security , 2016, pp. 308–318
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Int. Conf. on Artificial Intelligence and Statistics (AISTATS) , ser. Proceedings of Machine Learning Research, vol. 54, Fort Lauderdale, FL, USA, Apr 2017, pp. 1273–1282
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 1175–1191
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
D. Evans, V. Kolesnikov, and M. Rosulek, “A pragmatic introduction to secure multi-party computation,” Foundations and Trends® in Privacy and Security , vol. 2, no. 2-3, 2017
2017
Cited alongside, same era.
B. Jayaraman, L. Wang, D. Evans, and Q. Gu, “Distributed learning without distress: Privacy-preserving empirical risk minimization,” Advances in in Neural Information Processing Systems , pp. 6346–6357, 2018
2018
Cited alongside, same era.
P. Jiang and G. Agrawal, “A linear speedup analysis of distributed deep learning with sparse and quantized communication,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , 2018, pp. 2530–2541
2018
Cited alongside, same era.
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller, “Inverting gradients - how easy is it to break privacy in federated learning?” in Annual Conference on Neural Information Processing Systems, NeurIPS , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., 2020
2020
Later among the works it cites.
J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova, “Secure single-server aggregation with (poly) logarithmic overhead,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security , 2020, pp. 1253–1269
2020
Later among the works it cites.
Y.-S. Jeon, M. M. Amiri, J. Li, and H. V. Poor, “A compressive sensing approach for federated learning over massive mimo communication systems,” IEEE Transactions on Wireless Communications , vol. 20, no. 3, pp. 1990–2004, 2020
2020
Later among the works it cites.
J. Xu, W. Du, Y. Jin, W. He, and R. Cheng, “Ternary compression for communication-efficient federated learning,” IEEE Transactions on Neural Networks and Learning Systems , 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. U. Stich, J.-B. Cordonnier, and M. Jaggi, “Sparsified sgd with memory,” Advances in Neural Information Processing Systems: Annual Conference on Neural Information Processing Systems, NeurIPS , 2018
2018
Cited alongside, same era.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4510–4520
2018
Cited alongside, same era.
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning differentially private recurrent language models,” in Int. Conf. on Learning Representations , 2018
2018
Cited alongside, same era.
R. Ferreira, “A new look at Bernoulli’s inequality,” Proceedings of the American Mathematical Society , vol. 146, no. 3, pp. 1123–1129, 2018
2018
Cited alongside, same era.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 10, no. 2, pp. 1–19, 2019
2019
Cited alongside, same era.
M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,” in 2019 IEEE symposium on security and privacy (SP) . IEEE, 2019, pp. 739–753
2019
Cited alongside, same era.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” IEEE transactions on neural networks and learning systems , vol. 31, no. 9, pp. 3400–3413, 2019
2019
Cited alongside, same era.
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Sun, X. Ma, and R. Q. Hu, “Adaptive federated learning with gradient compression in uplink noma,” IEEE Transactions on Vehicular Technology , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
J. So, B. Güler, and A. S. Avestimehr, “Byzantine-resilient secure federated learning,” IEEE Journal on Selected Areas in Communications , 2020
2020
Later among the works it cites.
J. Xu, B. S. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang, “Federated learning for healthcare informatics,” Journal of Healthcare Informatics Research , vol. 5, no. 1, pp. 1–19, 2021
2021
Closest in time.
Y. Zhao and H. Sun, “Information theoretic secure aggregation with user dropouts,” in IEEE International Symposium on Information Theory, ISIT’21 , 2021
2021
Closest in time.
J. So, B. Güler, and A. S. Avestimehr, “Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,” IEEE Journal on Selected Areas in Information Theory , 2021
2021
Closest in time.
2021
Closest in time.
P. Kairouz and H. B. McMahan, “Advances and open problems in federated learning,” Foundations and Trends in Machine Learning , vol. 14, no. 1, 2021
2021
Closest in time.
——, “Codedprivateml: A fast and privacy-preserving framework for distributed machine learning,” IEEE Journal on Selected Areas in Information Theory , vol. 2, no. 1, pp. 441–451, 2021
2021
Closest in time.
J. So, R. E. Ali, B. Guler, J. Jiao, and S. Avestimehr, “Securing secure aggregation: Mitigating multi-round privacy leakage in federated learning,” IACR Cryptol. ePrint Arch. , p. 771, 2021. [Online]. Available: https://eprint.iacr.org/2021/771
2021
Closest in time.