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Learning generative models is challenging for a network edge node withlimited data and computing power. Since tasks in similar environments sharemodel similarity, it is plausible to leverage pre-trained generative modelsfrom the cloud or other edge nodes. Appealing to optimal transport theorytailored towards Wasserstein-1 generative adversarial networks (WGAN), thisstudy aims to develop a framework which systematically optimizes continuallearning of generative models using local data at the edge node whileexploiting adaptive coalescence of pre-trained generative models. Specifically,by treating the knowledge transfer from other nodes as Wasserstein ballscentered around their pre-trained models, continual learning of generativemodels is cast as a constrained optimization problem, which is further reducedto a Wasserstein-1 barycenter problem. A two-stage approach is devisedaccordingly: 1) The barycenters among the pre-trained models are computedoffline, where displacement interpolation is used as the theoretic foundationfor finding adaptive barycenters via a "recursive" WGAN configuration; 2) thebarycenter computed offline is used as meta-model initialization for continuallearning and then fast adaptation is carried out to find the generative modelusing the local samples at the target edge node. Finally, a weightternarization method, based on joint optimization of weights and threshold forquantization, is developed to compress the generative model further.

Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence