6G White Paper on Edge Intelligence
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Abstract (arXiv)
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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2020.
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Citing papers per year: 2021: 2; 2022: 5; 2023: 4; 2024: 1; 2025: 2.
14 papers.
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- 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
- 2023Green Edge AI: A Contemporary Surveycited by 24
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
- 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systemscited by 19
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
- 2021Roadmap for Edge AI: A Dagstuhl Perspectivecited by 4
- 2025SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functionscited by 3
- 2022Efficient Distributed DNNs in the Mobile-edge-cloud Continuumcited by 1
- 2025Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Imagescited by 1
- 2022Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Drivingcited by 0
- 2025SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions25 Jul 2025
- 2025Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Images24 Jun 2025
- 2024Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis17 Sep 2024
- 2023Green Edge AI: A Contemporary Survey1 Dec 2023
- 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems10 Nov 2023
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions3 Jul 2023
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6G2 Jan 2023
- 2022Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control26 Dec 2022
- 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions5 Aug 2022
- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration3 May 2022
- 2022Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving22 Mar 2022
- 2022Efficient Distributed DNNs in the Mobile-edge-cloud Continuum23 Feb 2022
- 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
- 2023Green Edge AI: A Contemporary Surveycited by 24
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
- 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systemscited by 19
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
- 2021Roadmap for Edge AI: A Dagstuhl Perspectivecited by 4
- 2025SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functionscited by 3
- 2022Efficient Distributed DNNs in the Mobile-edge-cloud Continuumcited by 1
- 2025Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Imagescited by 1
- 2022Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Drivingcited by 0
12 most cited of 14 All 14 (JSON)
- 2025SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions25 Jul 2025
- 2025Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Images24 Jun 2025
- 2024Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis17 Sep 2024
- 2023Green Edge AI: A Contemporary Survey1 Dec 2023
- 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems10 Nov 2023
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions3 Jul 2023
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6G2 Jan 2023
- 2022Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control26 Dec 2022
- 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions5 Aug 2022
- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration3 May 2022
- 2022Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving22 Mar 2022
- 2022Efficient Distributed DNNs in the Mobile-edge-cloud Continuum23 Feb 2022
12 newest of 14 All 14 (JSON)