  On-device training for personalized learning is a challenging research
problem. Being able to quickly adapt deep prediction models at the edge is
necessary to better suit personal user needs. However, adaptation on the edge
poses some questions on both the efficiency and sustainability of the learning
process and on the ability to work under shifting data distributions. Indeed,
naively fine-tuning a prediction model only on the newly available data results
in catastrophic forgetting, a sudden erasure of previously acquired knowledge.
In this paper, we detail the implementation and deployment of a hybrid
continual learning strategy (AR1*) on a native Android application for
real-time on-device personalization without forgetting. Our benchmark, based on
an extension of the CORe50 dataset, shows the efficiency and effectiveness of
our solution.
