Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning. (arXiv:2210.00053v2 [cs.LG] UPDATED)

Normalization is an important but understudied challenge in privacy-related
application domains such as federated learning (FL), differential privacy (DP),
and differentially private federated learning (DP-FL). While the unsuitability
of batch normalization for these domains has already been shown, the impact of
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, …

kernel machine machine learning networks privacy