A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency. (arXiv:2001.09782v3 [cs.DC] UPDATED)

Deep learning has achieved great success in many applications. However, its
deployment in practice has been hurdled by two issues: the privacy of data that
has to be aggregated centrally for model training and high communication
overhead due to transmission of a large amount of data usually geographically
distributed. Addressing both issues is challenging and most existing works
could not provide an efficient solution. In this paper, we develop FedPC, a
Federated Deep Learning Framework for Privacy Preservation and Communication …

communication dc framework privacy