  Federated learning is a distributed machine learning framework which enables
different parties to collaboratively train a model while protecting data
privacy and security. Due to model complexity, network unreliability and
connection in-stability, communication cost has became a major bottleneck for
applying federated learning to real-world applications. Current existing
strategies are either need to manual setting for hyperparameters, or break up
the original process into multiple steps, which make it hard to realize
end-to-end implementation. In this paper, we propose a novel compression
strategy called Residual Pooling Network (RPN). Our experiments show that RPN
not only reduce data transmission effectively, but also achieve almost the same
performance as compared to standard federated learning. Our new approach
performs as an end-to-end procedure, which should be readily applied to all
CNN-based model training scenarios for improvement of communication efficiency,
and hence make it easy to deploy in real-world application without much human
intervention.
