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Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolated data silos by cooperatively training models among organizations without breaching privacy and security.
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2021
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A. Z. Tan, H. Yu, L. Cui, and Q. Yang, “Towards personalized federated learning,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–17, 2022
2022
Closest in time.
J. Hong, H. Wang, Z. Wang, and J. Zhou, “Efficient split-mix federated learning for on-demand and in-situ customization,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=_QLmakITKg
2022
Closest in time.
2022
Closest in time.
Y. Liu, X. Zhang, Y. Kang, L. Li, T. Chen, M. Hong, and Q. Yang, “Fedbcd: A communication-efficient collaborative learning framework for distributed features,” IEEE Transactions on Signal Processing , pp. 1–12, 2022
2022
Closest in time.
2022
Closest in time.
C. Fu, X. Zhang, S. Ji, J. Chen, J. Wu, S. Guo, J. Zhou, A. X. Liu, and T. Wang, “Label inference attacks against vertical federated learning,” in 31st USENIX Security Symposium (USENIX Security 22) . Boston, MA: USENIX Association, 2022. [Online]. Available: https://www.usenix.org/conference/usenixsecurity22/presentation/fu
2022
Closest in time.
D. Chai, L. Wang, L. Fu, J. Zhang, K. Chen, and Q. Yang, “Practical lossless federated singular vector decomposition over billion-scale data,” 2022
2022
Closest in time.
2022
Closest in time.
Y. Kang, Y. Liu, and X. Liang, “Fedcvt: Semi-supervised vertical federated learning with cross-view training,” ACM Trans. Intell. Syst. Technol. , vol. 13, no. 4, may 2022. [Online]. Available: https://doi.org/10.1145/3510031
2022
Closest in time.
2022
Closest in time.
Z. Ren, L. Yang, and K. Chen, “Improving availability of vertical federated learning: Relaxing inference on non-overlapping data,” ACM Transactions on Intelligent Systems and Technology (TIST) , 2022
2022
Closest in time.
L. Wang, C. Huang, and X. Han, “Vertical federated knowledge transfer via representation distillation,” FL-IJCAI workshop , 2022
2022
Closest in time.
2022
Closest in time.
X. Gong, A. Sharma, S. Karanam, Z. Wu, T. Chen, D. Doermann, and A. Innanje, “Preserving privacy in federated learning with ensemble cross-domain knowledge distillation,” 2022
2022
Closest in time.
K. Matsuda, Y. Sasaki, C. Xiao, and M. Onizuka, “Fedme: Federated learning via model exchange,” in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) . SIAM, 2022, pp. 459–467
2022
Closest in time.
R. Liu, W. Fangzhao, W. Chuhan, W. Yanlin, L. Lingjuan, C. Hong, and X. Xing, “No one left behind: Inclusive federated learning over heterogeneous devices,” KDD , 2022
2022
Closest in time.
Y. Yang, X. Ye, and T. Sakurai, “Multi-view federated learning with data collaboration,” ser. ICMLC 2022. New York, NY, USA: Association for Computing Machinery, 2022, p. 178–183. [Online]. Available: https://doi.org/10.1145/3529836.3529904
2022
Closest in time.
K. Yuan, Z. Wu, and Q. Ling, “A byzantine-resilient dual subgradient method for vertical federated learning,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 4273–4277
2022
Closest in time.
A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, and R. Pedarsani, “Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization,” in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics , ser. Proceedings of Machine Learning Research, S. Chiappa and R. Calandra, Eds., vol. 108. PMLR, 26–28 Aug 2020, pp. 2021–2031
2031
Closest in time.