Project description
Secure and sustainable multimodal large models at the edge
Generative AI services often rely on centralised clouds, creating challenges for data privacy, energy consumption and response speed. Supported by the Marie Skłodowska-Curie Actions programme, the EDGELM project aims to address these issues by developing a framework for adapting and deploying multimodal large models across heterogeneous edge infrastructures. By utilising reinforcement and federated learning, the project will support efficient resource management and privacy-preserving model adaptation across diverse on-premises environments. EDGELM will also explore quantum-enhanced training methods and validate the framework in sensitive healthcare settings to ensure ethical compliance. Ultimately, the project aims to establish a roadmap for privacy-compliant and sustainable generative AI in Europe, accelerating the transition from centralised cloud dependence to secure and efficient edge intelligence.
Objective
Large models (LMs) powering generative Artificial Intelligence (AI) services are typically hosted in centralized cloud infrastructures. This paradigm introduces significant challenges related to data sovereignty, privacy protection, energy consumption, and transmission latency, severely restricting their suitability for real-time, privacy-sensitive applications. Despite recent advances, current solutions for LM compression and edge deployment remain insufficiently adaptive to heterogeneous hardware distributions, diverse multimodal model requirements, and practical deployment constraints.
The EDGELM project directly addresses these gaps by developing a context-aware, AI-driven framework that tailors LM compression strategies to heterogeneous edge-resource distributions, modality-specific compression characteristics, and application-specific accuracy constraints. Complementing this framework, an adaptive reinforcement-learning-assisted orchestration solution will be designed to optimize model cooperation and resource allocation, thereby improving inference latency and energy efficiency. Furthermore, EDGELM will integrate hybrid quantum-classical fine-tuning methods and federated learning techniques to augment model expressiveness and training efficiency within limited resource budgets. EDGELM’s practical effectiveness will be validated through real-world deployment in a privacy-sensitive healthcare scenario, ensuring compliance with data protection and ethical standards, while evaluating user acceptance and trust with expert psychological insights.
Supported by advanced edge-computing and quantum computing facilities at Politecnico di Torino, industrial expertise from Nextworks, and clinical input from healthcare partners, EDGELM will establish a robust methodology and practical roadmap for broader deployment of multimodal LMs at the network edge. This initiative supports Europe's transition toward privacy-compliant, sustainable, and efficient generative AI services.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology environmental engineering ecosystem-based management climatic change mitigation
- natural sciences computer and information sciences computer security data protection
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering computer hardware quantum computers
You need to log in or register to use this function
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.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
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
See all projects funded under this funding scheme
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-2025-PF
See all projects funded under this callCoordinator
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.
10129 Torino
Italy
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.