Specifically, the knowledge transferred from the cloud is in the form of a Dirichlet process prior distribution for the edge model parameters, and the edge ...

Specifically, the knowledge transferred from the cloud is in the form of a Dirichlet process prior distribution for the edge model parameters, and the edge ...

We develop a distributionally robust optimization (DRO)-based edge learning algorithm, where the uncertainty model is constructed to foster the synergy of ...

Specifically, the cloud transferred knowledge is in the form of a Dirichlet process prior distribution for the edge model parameters, and the edge device ...

Nov 29, 2020 · To tackle these challenges, we develop a distributionally robust optimization (DRO)-based edge learning algorithm, where the uncertainty model ...

We then use an Expectation-Maximization algorithm-inspired method to derive a convex relaxation, based on which we devise algorithms to learn the edge model.

Distributionally robust edge learning with dirichlet process prior. Z Zhang, Y Chen, J Zhang. 2020 IEEE 40th International Conference on Distributed Computing ...

4.1. INTRODUCTION. As noted before, the real-time edge intelligence can be achieved by the collaborative learn- ing across different edge nodes.

Zhaofeng Zhang, Yue Chen, Junshan Zhang. Figure 1 An illustration of the system model for distributionally robust edge learning with Dirichlet process prior.

View · Distributionally Robust Edge Learning with Dirichlet Process Prior. Conference Paper. Nov 2020. Zhaofeng Zhang · Yue Chen · Junshan Zhang · View.