  Federated learning has been showing as a promising approach in paving the
last mile of artificial intelligence, due to its great potential of solving the
data isolation problem in large scale machine learning. Particularly, with
consideration of the heterogeneity in practical edge computing systems,
asynchronous edge-cloud collaboration based federated learning can further
improve the learning efficiency by significantly reducing the straggler effect.
Despite no raw data sharing, the open architecture and extensive collaborations
of asynchronous federated learning (AFL) still give some malicious participants
great opportunities to infer other parties' training data, thus leading to
serious concerns of privacy. To achieve a rigorous privacy guarantee with high
utility, we investigate to secure asynchronous edge-cloud collaborative
federated learning with differential privacy, focusing on the impacts of
differential privacy on model convergence of AFL. Formally, we give the first
analysis on the model convergence of AFL under DP and propose a multi-stage
adjustable private algorithm (MAPA) to improve the trade-off between model
utility and privacy by dynamically adjusting both the noise scale and the
learning rate. Through extensive simulations and real-world experiments with an
edge-could testbed, we demonstrate that MAPA significantly improves both the
model accuracy and convergence speed with sufficient privacy guarantee.
