Vertical Federated Learning: Challenges, Methodologies and Experiments

A. Singh, P. Vepakomma, O. Gupta, and R. Raskar, “Detailed comparison of communication efficiency of split learning and federated learning,” Arxiv , 2019. [Online]. Available: http://arxiv.org/abs/1909.09145

Original

1909

Earlier work this paper cites.

J. Jiang, F. Fu, T. Yang, and B. Cui, “SketchML: Accelerating distributed machine learning with data sketches,” in Proc. International Conference on Management of Data , Houston, TX, USA, 2018, pp. 1269–1284

2018

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 , vol. 10, no. 2, pp. 12:1–12:19, 2019

2019

Earlier work this paper cites.

K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. S. Quek, and H. V. Poor, “Federated learning with differential privacy: Algorithms and performance analysis,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 3454–3469, 2020

2020

Earlier work this paper cites.

C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. S. Quek, and H. V. Poor, “On safeguarding privacy and security in the framework of federated learning,” IEEE Network , vol. 34, no. 4, pp. 242–248, 2020

2020

Earlier work this paper cites.

Y. Cheng, Y. Liu, T. Chen, and Q. Yang, “Federated learning for privacy-preserving AI,” Communications of the ACM , vol. 63, no. 12, pp. 33–36, Nov. 2020

2020

Cited alongside, same era.

L. Lu and N. Ding, “Multi-party private set intersection in vertical federated learning,” in Proc. IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) , Guangzhou, China, 2020, pp. 707–714

2020

Cited alongside, same era.

B. Gu, Z. Dang, X. Li, and H. Huang, “Federated doubly stochastic kernel learning for vertically partitioned data,” in Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , Virtual Event, CA, USA, Aug. 2020, pp. 2483–2493

2020

Cited alongside, same era.

S. Abuadbba, K. Kim, M. Kim, C. Thapa, S. A. Çamtepe, Y. Gao, H. Kim, and S. Nepal, “Can we use split learning on 1d CNN models for privacy preserving training?” in Proc. ACM Asia Conference on Computer and Communications Security (ASIACCS) , Taipei, Taiwan, Oct. 2020, pp. 305–318

R. Yu and P. Li, “Toward resource-efficient federated learning in mobile edge computing,” IEEE Network , vol. 35, no. 1, pp. 148–155, 2021

2021

Later among the works it cites.

F. Fu, Y. Shao, L. Yu, J. Jiang, H. Xue, Y. Tao, and B. Cui, “VF 2

2021

Later among the works it cites.

X. Jin, P. Chen, C. Hsu, C. Yu, and T. Chen, “CAFE: Catastrophic data leakage in vertical federated learning,” in Proc. Thirty-fifth Conference on Neural Information Processing Systems (NeurPIS) , Virtual Event, Dec. 2021

2021

Later among the works it cites.

Q. Zhang, B. Gu, C. Deng, and H. Huang, “Secure bilevel asynchronous vertical federated learning with backward updating,” in Proc. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) , Virtual Event, Feb. 2021, pp. 10 896–10 904

2021

Later among the works it cites.