DECICE: Device-Edge-Cloud Intelligent Collaboration Framework
DECICE is a Horizon Europe project that is developing an AI-enabled open and portable management framework for automatic and adaptive optimization and deployment of applications in computing continuum encompassing from IoT sensors on the Edge to large-scale Cloud / HPC computing infrastructures. In this paper, we describe the DECICE framework and architecture. Furthermore, we highlight use-cases for framework evaluation: intelligent traffic intersection, magnetic resonance imaging, and emergency response.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
The rise of cloud-device collaborative computing has enabled intelligent services to be delivered across distributed edge devices while leveraging centralized cloud resources. In this paradigm, federated learning (FL) ha…
Federated LearningContinual LearningEdge-Cloud Collaborated Object Detection via Difficult-Case Discriminator
As one of the basic tasks of computer vision, object detection has been widely used in many intelligent applications. However, object detection algorithms are usually heavyweight in computation, hindering their implement…
Objectobject-detectionObject DetectionBackpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration
In our increasingly interconnected world, where intelligent devices continually amass copious personalized multi-modal data, a pressing need arises to deliver high-quality, personalized device-aware services. However, th…
Question AnsweringVideo Question AnsweringFedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework
With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. To improve the performance of the …
Federated LearningModel OptimizationCollaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to capture real-time user preferences greatl…
Collaborative InferenceDevice-Cloud CollaborationEdge-computingReranking