相关论文: End-Cloud Collaboration Framework for Advanced AI …
In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more…
Edge-cloud synergies provide a promising paradigm for privacy-preserving deployment of foundation models, where lightweight on-device models adapt to domain-specific data and cloud-hosted models coordinate knowledge sharing. However, in…
Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud. However, this approach introduces a number of privacy and efficiency challenges, as the cloud operator…
Cloud computing technology is an emerging new computing paradigm for delivering computing services. Although it still in its early stage, it has changed the way how many applications are developed and accessed. This computing approach…
The edge-cloud continuum has emerged as a transformative paradigm that meets the growing demand for low-latency, scalable, end-to-end service delivery by integrating decentralized edge resources with centralized cloud infrastructures.…
The scale of the global edge AI market continues to grow. The current technical challenges that hinder the large-scale replication of edge AI are mainly small samples on the edge and heterogeneity of edge data. In addition, edge AI…
The development of artificial intelligence demands that models incrementally update knowledge by Continual Learning (CL) to adapt to open-world environments. To meet privacy and security requirements, Continual Unlearning (CU) emerges as an…
Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for…
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative,…
Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the…
The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end users activities. Data collected by numerous devices present in the IoT infrastructure can be…
The rapid aging of global populations has created an urgent need for intelligent healthcare monitoring systems to ensure the safety of elderly individuals living independently. Existing cloud-centric platforms face critical limitations,…
We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge…
The ubiquitous use of IoT and machine learning applications is creating large amounts of data that require accurate and real-time processing. Although edge-based smart data processing can be enabled by deploying pretrained models, the…
Modern Internet services are increasingly leveraging on cloud computing for flexible, elastic and on-demand provision. Typically, Quality of Service (QoS) of cloud-based services can be tuned using different underlying cloud configurations…
With tremendous efforts on developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical…
Aiming to support a cross-sector and cross-border eGovernance paradigm for sharing common public services, this paper introduces an AI-enhanced solution that enables beneficiaries to participate in a decenntralized network for effective big…
This paper introduces A2C, a multi-stage collaborative decision framework designed to enable robust decision-making within human-AI teams. Drawing inspiration from concepts such as rejection learning and learning to defer, A2C incorporates…
The conspicuous lack of cloud-specific security certifications, in addition to the existing market fragmentation, hinder transparency and accountability in the provision and usage of European cloud services. Both issues ultimately reflect…
In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous. This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative…