Over-the-Air Federated Learning with Enhanced Privacy. (arXiv:2212.11486v1 [cs.CR])

Federated learning (FL) has emerged as a promising learning paradigm in which
only local model parameters (gradients) are shared. Private user data never
leaves the local devices thus preserving data privacy. However, recent research
has shown that even when local data is never shared by a user, exchanging model
parameters without protection can also leak private information. Moreover, in
wireless systems, the frequent transmission of model parameters can cause
tremendous bandwidth consumption and network congestion when the model is
large. …

federated learning privacy