6G White Paper on Edge Intelligence
- URL: http://arxiv.org/abs/2004.14850v1
- Date: Thu, 30 Apr 2020 15:02:08 GMT
- Title: 6G White Paper on Edge Intelligence
- Authors: Ella Peltonen, Mehdi Bennis, Michele Capobianco, Merouane Debbah,
Aaron Ding, Felipe Gil-Casti\~neira, Marko Jurmu, Teemu Karvonen, Markus
Kelanti, Adrian Kliks, Teemu Lepp\"anen, Lauri Lov\'en, Tommi Mikkonen,
Ashwin Rao, Sumudu Samarakoon, Kari Sepp\"anen, Pawe{\l} Sroka, Sasu Tarkoma,
Tingting Yang
- Abstract summary: We focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects.
We envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.
- Score: 28.854456451854546
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this white paper we provide a vision for 6G Edge Intelligence. Moving
towards 5G and beyond the future 6G networks, intelligent solutions utilizing
data-driven machine learning and artificial intelligence become crucial for
several real-world applications including but not limited to, more efficient
manufacturing, novel personal smart device environments and experiences, urban
computing and autonomous traffic settings. We present edge computing along with
other 6G enablers as a key component to establish the future 2030 intelligent
Internet technologies as shown in this series of 6G White Papers.
In this white paper, we focus in the domains of edge computing infrastructure
and platforms, data and edge network management, software development for edge,
and real-time and distributed training of ML/AI algorithms, along with
security, privacy, pricing, and end-user aspects. We discuss the key enablers
and challenges and identify the key research questions for the development of
the Intelligent Edge services. As a main outcome of this white paper, we
envision a transition from Internet of Things to Intelligent Internet of
Intelligent Things and provide a roadmap for development of 6G Intelligent
Edge.
Related papers
- Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End
Collaboration [56.330705072736166]
We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm.
As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
arXiv Detail & Related papers (2023-10-26T15:19:40Z) - Large Language Models Empowered Autonomous Edge AI for Connected
Intelligence [51.269276328087855]
Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence.
This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
arXiv Detail & Related papers (2023-07-06T05:16:55Z) - Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks.
There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks.
In this article, we investigate the optimization approaches that can effectively address the challenges.
arXiv Detail & Related papers (2023-03-15T02:18:21Z) - Deep Edge Intelligence: Architecture, Key Features, Enabling
Technologies and Challenges [0.0]
We present a novel computing vision named Deep Edge Intelligence (DEI)
It employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc.
It aims to provision reliable and secure intelligence services to every person and organisation at any place with better user experience.
arXiv Detail & Related papers (2022-10-24T04:18:57Z) - In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile
Networks [61.416494781759326]
In-situ model downloading aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network.
A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level.
We propose a 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
arXiv Detail & Related papers (2022-10-07T13:41:15Z) - Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and
Applications [39.223546118441476]
6G will revolutionize the evolution of wireless from "connected things" to "connected intelligence"
Deep learning and big data analytics based AI systems require tremendous computation and communication resources.
edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence.
arXiv Detail & Related papers (2021-11-24T11:47:16Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z) - Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.
In this article, we identify the key requirements and challenges of edge-native AI in 6G.
arXiv Detail & Related papers (2020-10-01T02:16:40Z) - Federated Learning for 6G Communications: Challenges, Methods, and
Future Directions [71.31783903289273]
We introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G.
We describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.
arXiv Detail & Related papers (2020-06-04T15:17:19Z) - Communication-Efficient Edge AI: Algorithms and Systems [39.28788394839187]
Wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data.
Such enormous data cannot all be sent from end devices to the cloud for processing.
By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
arXiv Detail & Related papers (2020-02-22T09:27:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.