Cross-Silo Federated Learning: Challenges and Opportunities

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2013

Earlier work this paper cites.

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

Earlier work this paper cites.

K. Pillutla, S. M. Kakade, and Z. Harchaoui, “Robust aggregation for federated learning,” arXiv preprint arXiv:1912.13445 , 2019

Original

2019

Earlier work this paper cites.

Y. Sarikaya and O. Ercetin, “Motivating workers in federated learning: A stackelberg game perspective,” IEEE Networking Letters , vol. 2, no. 1, pp. 23–27, 2019

2019

Earlier work this paper cites.

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2019

Earlier work this paper cites.

L. Li, Y. Fan, M. Tse, and K.-Y. Lin, “A review of applications in federated learning,” Computers & Industrial Engineering , vol. 149, p. 106854, 2020

2020

Cited alongside, same era.

T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020

2020

Cited alongside, same era.

P. Kairouz et al. , “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1–2, pp. 1–210, 2021

2021

Cited alongside, same era.

H. Zhu, J. Xu, S. Liu, and Y. Jin, “Federated learning on non-iid data: A survey,” Neurocomputing , vol. 465, pp. 371–390, 2021

2021

Cited alongside, same era.

X. Ouyang, Z. Xie, J. Zhou, J. Huang, and G. Xing, “Clusterfl: a similarity-aware federated learning system for human activity recognition,” in Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services , 2021, pp. 54–66

V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Generation Computer Systems , vol. 115, pp. 619–640, 2021

2021

Later among the works it cites.

K. Donahue and J. Kleinberg, “Model-sharing games: Analyzing federated learning under voluntary participation,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 6, pp. 5303–5311, May 2021

2021

Later among the works it cites.

K. Wei, J. Li, C. Ma, M. Ding, S. Wei, F. Wu, G. Chen, and T. Ranbaduge, “Vertical federated learning: Challenges, methodologies and experiments,” arXiv preprint arXiv:2202.04309 , 2022

Original

2022

Closest in time.

P. Sun, X. Chen, G. Liao, and J. Huang, “A profit-maximizing model marketplace with differentially private federated learning,” in Proc. of IEEE INFOCOM , 2022, pp. 1439–1448

2022

Closest in time.