Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise. (arXiv:2211.15893v1 [cs.LG])

Federated learning seeks to address the issue of isolated data islands by
making clients disclose only their local training models. However, it was
demonstrated that private information could still be inferred by analyzing
local model parameters, such as deep neural network model weights. Recently,
differential privacy has been applied to federated learning to protect data
privacy, but the noise added may degrade the learning performance much.
Typically, in previous work, training parameters were clipped equally and
noises were added uniformly. …

federated learning