  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.
