Decentralized Federated learning is a distributed edge intelligence framework by exchanging parameter updates instead of training data among participators, in order to retrain or fine-tune deep learning models for mobile intelligent applications. Considering the various topologies of edge networks in mobile internet, the impact of transmission delay of updates during model training is non-negligible for data-intensive intelligent applications on mobile devices, e.g., intelligent medical services, automated driving vehicles, etc.. To address this problem, we analyze the impact of delayed updates for decentralized federated learning, and provide a theoretical bound for these updates to achieve model convergence. Within the theoretical bound of updating period, the latest versions for the delayed updates are reused to continue aggregation, in case the model parameters from a specific neighbor are not collected or updated in time.

翻译：去中心化联邦学习是一种分布式边缘智能框架，通过参与方之间交换参数更新而非训练数据，来重新训练或微调面向移动智能应用的深度学习模型。考虑到移动互联网中边缘网络拓扑结构的多样性，在数据密集型移动设备智能应用（如智能医疗服务、自动驾驶车辆等）中，模型训练过程中参数更新的传输延迟影响不可忽视。针对这一问题，我们分析了延迟更新对去中心化联邦学习的影响，并为这些更新实现模型收敛提供了理论界限。在更新周期的理论界限内，当特定邻居节点的模型参数未能及时收集或更新时，可重新利用延迟更新的最新版本继续进行聚合。