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.

翻译：联邦学习（FL）是一种前景广阔的去中心化学习机制，但仍面临两大主要挑战，即隐私泄露和系统效率。本文从网络信息论角度重新审视联邦学习系统，受网络编码（NC）启发，提出了一种原创性的联邦学习通信框架FedNC。FedNC的核心思想是，在本地模型上传进行聚合前，通过对原始参数进行随机线性组合来混合本地模型的信息。得益于编码方案的优势，理论分析与实验结果表明，FedNC在安全性、效率和鲁棒性等多个重要方面提升了传统联邦学习的性能。据我们所知，这是首个将网络编码引入联邦学习的框架。随着联邦学习在实际网络框架中的持续演进，可基于FedNC进一步设计更多变体。