A novel federated learning approach based on the confidence of federated Kalman filters

Author:

Hu KaiORCID,Wu Jiasheng,Weng Liguo,Zhang Yanwen,Zheng Fei,Pang Zichao,Xia Min

Funder

National Natural Science Foundation of China

key special project of the National Key R&D Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Reference37 articles.

1. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2):1–19

2. Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 37(3):50–60

3. Kairouz P, McMahan HB, Avent B, Bellet A, Bennis, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et al Advances and open problems in federated learning, arXiv preprint: arXiv:1912.04977

4. Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Kone£n`y J, Mazzocchi S, McMahan HB et al Towards federated learning at scale: System design, arXiv preprint: arXiv:1902.01046

5. J. Kone£n`y HB, McMahan DR, Richtárik P Federated optimization: Distributed machine learning for on-device intelligence, arXiv preprint: arXiv:1610.02527

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