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Federated learning enables machine learning algorithms to be trained over a network of multiple decentralized edge devices without requiring the exchange of local datasets.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. Nitin Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, “Advances and open problems in federated learning,” [Online]. Available: arXiv: 1912.04977
1912
Earlier work this paper cites.
R. E. Shostak L. Lamport and M. C. Pease. “The Byzantine generals problem,” ACM Trans. Program. Lang. Syst.,
1982
Earlier work this paper cites.
V. Vapnik, “Principles of risk minimization for learning theory,” in Proc. Adv. Neural Info. Process. Syst. (NeurIPS)
1992
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE
1998
Earlier work this paper cites.
P. Milgrom and I. Segal, “Envelope theorems for arbitrary choice sets,” Econometrica
2002
Earlier work this paper cites.
D. C. Parkes, and J. Shneidman, “Distributed implementations of Vickrey-Clarke-Groves mechanisms,” 2004
2004
Earlier work this paper cites.
R. D. Nowak M. G. Rabbat, “Quantized incremental algorithms for distributed optimization,” IEEE J. Sel. Areas Commun
2005
Earlier work this paper cites.
J. Feigenbaum, R. Sami, and S. Shenker, “Mechanism design for policy routing,” Distributed Computing
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
2006
Earlier work this paper cites.
V. V. Vazirani, N. Nisanm, T. Roughgarden, and E. Tardos, Algorithmic Game Theory
2007
Earlier work this paper cites.
A. Petcu, B. Faltings, and D. C. Parkes, “M-DPOP: Faithful distributed implementation of efficient social choice problems.” J. Artif. Intell. Res. (JAIR)
2008
Earlier work this paper cites.
K. Sridharan, S. Shalev-Shwartz, and N. Srebro, “Fast rates for regularized objectives,” in Proc. Adv. Neural Info. Process. Syst. (NeurIPS)
2008
Earlier work this paper cites.
S. Song, K. Chaudhuri and A. D. Sarwate, “Stochastic gradient descent with differentially private updates,” in Proc. IEEE Global Conference on Signal and Information Processing
2013
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Bun and T. Steinke. “Concentrated differential privacy: Simplifications, extensions, and lower bounds,” in Proc. Theory of Cryptography Conference
2016
Earlier work this paper cites.
V. Smith, C. K. Chiang, M. Sanjabi, and A. Talwalkar, “Federated multi-task learning,” In Advances in Neural Information Processing Systems, pp. 4424–4434. 2017
2017
Earlier work this paper cites.
T. Tanaka, F. Farokhi and C. Langbort, “Faithful implementations of distributed algorithms and control laws,” IEEE Trans. Control Netw. Sys
2017
Cited alongside, same era.
B. Jayaraman, L. Wang, D. Evans, and Q. Gu, “Distributed learning without distress: Privacy-preserving empirical risk minimization,” Proc. Advances in Neural Info. Process. Sys. (NIPS)
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)
2019
Cited alongside, same era.
S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, and Y. Zhou, “A hybrid approach to privacy-preserving federated learning,” in Proc. ACM Workshop on Artificial Intelligence and Security (AISec)
2019
Cited alongside, same era.
R. Zeng, S. Zhang, J. Wang, and X. Chu, “Fmore: An incentive scheme of multi-dimensional auction for federated learning in mec,” in Proc. IEEE ICDCS
2020
Later among the works it cites.
R. H. L. Sim, Y. Zhang, M. C. Chan, and B. K. H. Low, “Collaborative machine learning with incentive-aware model rewards,” in Proc. International Conference on Machine Learning (ICML)
2020
Later among the works it cites.
S. R. Pandey, N. H. Tran, M. Bennis, Y. K. Tun, A. Manzoor, and C. S. Hong, “A crowdsourcing framework for on-device federated learning,” IEEE Trans. Wireless Commun
2020
Later among the works it cites.
Y. Jiao, P. Wang, D. Niyato, B. Lin, and D. I. Kim, “Toward an automated auction framework for wireless federated learning services market,” IEEE Trans. Mobile Comput
2020
Later among the works it cites.
Y. Zhan, P. Li, Z. Qu, D. Zeng, and S. Guo, “A learning-based incentive mechanism for federated learning,” IEEE Internet Things J
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2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Wang et al
2019
Cited alongside, same era.
J. Weng, J. Weng, J. Zhang, M. Li, Y. Zhang, and W. Luo, “Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive,” IEEE Transactions on Dependable and Secure Computing
2019
Cited alongside, same era.
J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang, “Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory,” IEEE Internet Things J
2019
Cited alongside, same era.
A. Ghorbani and J. Zou, “Data shapley: Equitable valuation of data for machine learning,” in Proc. International Conference on Machine Learning (ICML)
2019
Cited alongside, same era.
M. J., Wainwright, High-dimensional statistics: A non-asymptotic viewpoint
2019
Cited alongside, same era.
R. Kelly, “Internet of Things data to top 1.6 zettabytes by 2020,” Apr. 2015. [Online]. Available: https://campustechnology.com/articles/2015/04/15/internet-of-things-data-to-top-1-6-zettabytes-by-2020.aspx
2020
Cited alongside, same era.
2020
Later among the works it cites.
Y. Sarikaya and O. Ercetin, “Motivating workers in federated learning: A Stackelberg game perspective,” IEEE Netw. Lett
2020
Later among the works it cites.
H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, and Q. Yang, “A sustainable incentive scheme for federated learning,” IEEE Intelligent Systems
2020
Later among the works it cites.
H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, and Q. Yang, “A fairness-aware incentive scheme for federated learning,” in Proc. AAAI/ACM AIES,
2020
Later among the works it cites.
N. Ding, Z. Fang, and J. Huang, “Optimal contract design for efficient federated learning with multi-dimensional private information,” IEEE J. Sel. Areas Commun
2020
Later among the works it cites.
M. Zhang, E. Wei, and R. Berry, “Faithful federated learning,” in Proc. 16th Workshop Econ. Netw., Syst. Comput. (NetEcon)
2021
Closest in time.
X. Cao, J. Jinyuan, and N. Z. Gong, “Provably Secure Federated Learning against Malicious Clients,” in Proc. AAAI Conf. Artificial Intelligence
2021
Closest in time.
T. D. Nguyen, et al
2021
Closest in time.
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” IEEE Trans Wireless Commun
2021
Closest in time.
Y. Zhan, J. Zhang, Z. Hong, L. Wu, P. Li, and S. Guo, “A survey of incentive mechanism design for federated learning”, IEEE Transactions on Emerging Topics in Computing,
2021
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P. Sun, H. Che, Z. Wang, Y. Wang, T. Wang, L. Wu, and H. Shao. “Pain-FL: Personalized privacy-preserving incentive for federated learning,” to appear in IEEE J. Sel. Areas Commun
2021
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