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.

边缘设备资源有限，难以将深度学习模型推广到各种环境和任务。本文提出了一种具有开放集识别能力的边缘云协同系统 EdgeFM，通过选择性上传未标记的数据来查询云端的基础模型 (FM)，并为边缘模型定制特定的知识和体系结构，同时 EdgeFM 在运行时考虑数据不确定性和动态网络变化进行动态模型切换，以确保准确性始终接近原始 FM。我们在两个边缘平台上使用两个 FM 实现了 EdgeFM。我们在三个公共数据集和两个自采集数据集上评估了 EdgeFM，结果表明与基准相比，EdgeFM 可以将端到端延迟缩短多达 3.2 倍，并实现 34.3% 的准确性提高。