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Figure
3.
System model of FLMSS with the case of
[1] |
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436–444. doi: 10.1038/nature14539
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[2] |
Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016: 779–788.
|
[3] |
Minaee S, Kalchbrenner N, Cambria E, et al. Deep learning: Based text classification: A comprehensive review. ACM Computing Surveys, 2021, 54 (3): 1–40. doi: 10.1145/3439726
|
[4] |
Lee M, Sanz L R D, Barra A, et al. Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning. Nature Communications, 2022, 13: 1064. doi: 10.1038/s41467-022-28451-0
|
[5] |
Wright L G, Onodera T, Stein M M, et al. Deep physical neural networks trained with backpropagation. Nature, 2022, 601: 549–555. doi: 10.1038/s41586-021-04223-6
|
[6] |
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 1–9.
|
[7] |
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016: 770–778.
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[8] |
McMahan H B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data. arXiv: 1602.05629, 2016.
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[9] |
Nasr M, Shokri R, Houmansadr A. Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE Symposium on Security and Privacy (SP). San Francisco, USA: IEEE, 2019: 739–753.
|
[10] |
Wang Z, Song M, Zhang Z, et al. Beyond inferring class representatives: User-level privacy leakage from federated learning. In: IEEE INFOCOM 2019—IEEE Conference on Computer Communications. Paris, France: IEEE, 2019: 2512–2520.
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[11] |
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[12] |
Hitaj B, Ateniese G, Perez-Cruz F. Deep models under the GAN: Information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC Confe |