Private Aggregation in Wireless Federated Learning with Heterogeneous
  Clusters
Maximilian Egger Christoph Hofmeister Antonia Wachter-Zeh Rawad Bitar
Federated learning collaboratively trains a neural network on privately owned
data held by several participating clients. The gradient descent algorithm, a
well-known and popular iterative optimization procedure, is run to train the
neural network. Every client uses its local data to compute partial gradients
and sends it to the federator which aggregates the results. Privacy of the
clients' data is a major concern. In fact, observing the partial gradients can
be enough to reveal the clients' data. Private aggregation schemes have been
investigated to tackle the privacy problem in federated learning where all the
users are connected to each other and to the federator. In this paper, we
consider a wireless system architecture where clients are only connected to the
federator via base stations. We derive fundamental limits on the communication
cost when information-theoretic privacy is required, and introduce and analyze
a private aggregation scheme tailored for this setting.