跨域场景下的联邦路由机制设计

随着多网融合的发展，独立自治域网络之间的高效互联互通已成为一个关键理论技术问题。由于域内信息的私密性，各自治域之间不会共享相关的数据，传统的域间路由协议无法提供高效的跨域路由机制。为克服现有协议存在的不足，提出了一种基于机器学习的联邦路由机制，该机制通过各自治域的神经网络模型和参数隐式地共享域内信息，不仅可打破现存的数据孤岛问题，还可极大地减少域间信息共享所需要传输的数据量，进而降低全网路由信息收敛时延，基于该联邦路由机制，边界路由器也能够根据全网状态信息制定全局的优化路由转发策略。

Design of federated routing mechanism in cross-domain scenario

With the development of multi-network integration,how to ensure efficient interconnections among multiple independent network domains is becoming a key problem.Traditional interdomain routing protocol fails due to the limitation of domain information privacy,where each autonomous domain doesn’t share any specific intra-domain information.A machine learning-based federated routing mechanism was proposed to overcome the existing shortcomings.Each autonomous domain shares intra-domain information implicitly via neural network models and parameters.It not only breaks data islands problems but also greatly reduces the amount of transmitted data shared between domains,then decreases convergence delay of entire network information.Based on the federated routing mechanism,border routers can formulate global optimal routing strategies according to the status of entire network.