  Deep Learning (DL) models have been widely deployed on IoT devices with the
help of advancements in DL algorithms and chips. However, the limited resources
of edge devices make these on-device DL models hard to be generalizable to
diverse environments and tasks. Although the recently emerged foundation models
(FMs) show impressive generalization power, how to effectively leverage the
rich knowledge of FMs on resource-limited edge devices is still not explored.
In this paper, we propose EdgeFM, a novel edge-cloud cooperative system with
open-set recognition capability. EdgeFM selectively uploads unlabeled data to
query the FM on the cloud and customizes the specific knowledge and
architectures for edge models. Meanwhile, EdgeFM conducts dynamic model
switching at run-time taking into account both data uncertainty and dynamic
network variations, which ensures the accuracy always close to the original FM.
We implement EdgeFM using two FMs on two edge platforms. We evaluate EdgeFM on
three public datasets and two self-collected datasets. Results show that EdgeFM
can reduce the end-to-end latency up to 3.2x and achieve 34.3% accuracy
increase compared with the baseline.
