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Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning. (arXiv:2210.00053v1 [cs.LG])
Oct. 4, 2022, 1:20 a.m. | Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis
cs.CR updates on arXiv.org arxiv.org
Normalization is an important but understudied challenge in privacy-related
application domains such as federated learning (FL) and differential privacy
(DP). While the unsuitability of batch normalization for FL and DP has already
been shown, the impact of the other normalization methods on the performance of
federated or differentially private models is not well-known. To address this,
we draw a performance comparison among layer normalization (LayerNorm), group
normalization (GroupNorm), and the recently proposed kernel normalization
(KernelNorm) in FL and DP settings. …
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