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 packets, 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, throughput, 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 applications and variants can be further designed based on FedNC.

通过以网络信息理论为出发点，设计了FedNC，一种融合了网络编码概念的联邦学习通信框架，该框架能够提高传统联邦学习的安全性、吞吐量和鲁棒性。在此基础上可以进一步设计更多的应用和变体。

FedNC: 一种受网络编码启发的安全高效联邦学习方法