Project description
Distributed network intelligence for 6G mobile edge systems
Network intelligence will play a crucial role in 6G mobile edge systems, leveraging distributed machine learning to manage devices in real-time. The varying conditions caused by user mobility require rapid model convergence for timely decision-making. Supported by the Marie Skłodowska-Curie Actions programme, the DRIVE project aims to establish a systematic design rooted in solid theoretical foundations and validated through prototypes. The project will enhance distributed network intelligence by developing a framework to assess how system parameters affect model training efficiency. It will also design a distributed model training method using personalised federated learning and create a prototype system to implement these solutions, advancing the capabilities of mobile edge systems.
Objective
Network intelligence is envisioned as the cornerstone of 6G mobile edge systems. By deploying distributed machine learning algorithms at end-user terminals, the network can harness collective intelligence from vast data sources, enabling the real-time management of numerous devices in dynamic environments. However, users’ mobility nature renders the end-user terminals constantly encountering varying physical conditions, necessitating fast convergence of the distributed model training for timely decision-making. To accomplish this target, this project aims to deliver a systematic design, grounded in solid theoretical foundations and validated through real-world prototypes, that advances the understanding, optimization, and implementation of distributed network intelligence in mobile edge systems. The objectives are fourfold: (1) establish a theoretical framework for assessing the impact of system parameters across different layers on the efficiency of model training and identifying the dominating factors that constrain the training performance; (2) develop a goal-oriented status information exchange mechanism to enable timely update of status information across the interconnected terminals; (3) design a novel distributed model training method, utilizing personalized federated learning and high-order distributed optimization methods to stabilize the model update and accelerate the convergence process; and (4) build a comprehensive prototype system that implements the proposed solutions and verify their practical efficacy. These innovations will not only enhance the interpretability of network intelligence but also improve its operability. The expected outcome of this project shall forge forward the studies in distributed machine learning as well as network communication theory, demonstrating that the innovative fusion of two traditionally hitherto unrelated fields of study will propel and facilitate the rapid development and deployment of network intelligence in 6G.
Fields of science (EuroSciVoc)
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
EX4 4QJ Exeter
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.