  Federated Learning (FL) is a promising distributed learning mechanism which
still faces two major challenges, namely privacy breaches and system
efficiency. In this work, we reconceptualize the FL system from the perspective
of network information theory, and formulate an original FL communication
framework, FedNC, which is inspired by Network Coding (NC). The main idea of
FedNC is mixing the information of the local models by making random linear
combinations of the original parameters, before uploading for further
aggregation. Due to the benefits of the coding scheme, both theoretical and
experimental analysis indicate that FedNC improves the performance of
traditional FL in several important ways, including security, efficiency, and
robustness. To the best of our knowledge, this is the first framework where NC
is introduced in FL. As FL continues to evolve within practical network
frameworks, more variants can be further designed based on FedNC.
