  Learning generative models is challenging for a network edge node with
limited data and computing power. Since tasks in similar environments share
model similarity, it is plausible to leverage pre-trained generative models
from the cloud or other edge nodes. Appealing to optimal transport theory
tailored towards Wasserstein-1 generative adversarial networks (WGAN), this
study aims to develop a framework which systematically optimizes continual
learning of generative models using local data at the edge node while
exploiting adaptive coalescence of pre-trained generative models. Specifically,
by treating the knowledge transfer from other nodes as Wasserstein balls
centered around their pre-trained models, continual learning of generative
models is cast as a constrained optimization problem, which is further reduced
to a Wasserstein-1 barycenter problem. A two-stage approach is devised
accordingly: 1) The barycenters among the pre-trained models are computed
offline, where displacement interpolation is used as the theoretic foundation
for finding adaptive barycenters via a "recursive" WGAN configuration; 2) the
barycenter computed offline is used as meta-model initialization for continual
learning and then fast adaptation is carried out to find the generative model
using the local samples at the target edge node. Finally, a weight
ternarization method, based on joint optimization of weights and threshold for
quantization, is developed to compress the generative model further.
