  Federated learning is a distributed learning method to train a shared model
by aggregating the locally-computed gradient updates. In federated learning,
bandwidth and privacy are two main concerns of gradient updates transmission.
This paper proposes an end-to-end encrypted neural network for gradient updates
transmission. This network first encodes the input gradient updates to a
lower-dimension space in each client, which significantly mitigates the
pressure of data communication in federated learning. The encoded gradient
updates are directly recovered as a whole, i.e. the aggregated gradient updates
of the trained model, in the decoding layers of the network on the server. In
this way, gradient updates encrypted in each client are not only prevented from
interception during communication, but also unknown to the server. Based on the
encrypted neural network, a novel federated learning framework is designed in
real applications. Experimental results show that the proposed network can
effectively achieve two goals, privacy protection and data compression, under a
little sacrifice of the model accuracy in federated learning.
