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Federated Learning for Antimicrobial Resistance Surveillance

Objective

FLAMR tackles the global health threat of antimicrobial resistance (AMR) by developing and validating a next-generation, federated reinforcement learning (FRL) system for near real-time surveillance calibrated for ESKAPEE+ pathogens. The project integrates human, veterinary, and environmental AMR datasets, being the first to combine FRL with multimodal diagnostics and community-based governance for AMR surveillance, particularly useful in resource-constrained settings. Its objectives are to (1) develop, deploy, and validate a privacy-by-design FRL architecture for AMR data integration across ≥12 labs in Estonia, Romania, Colombia, and Sri Lanka; (2) strengthen diagnostic capacities for bacteriological and metagenomic surveillance through the examination and sequencing of >400 field samples and the characterization of phage-based alternatives; and (3) co-develop AMR stewardship, governance, and education toolkits to empower local communities and authorities.
FLAMR combines advanced AI, metagenomics, and bioinformatics with social science and participatory governance to test whether FRL can outperform conventional surveillance pipelines in accuracy, interpretability, and inclusiveness. Technical leaders design and deploy the FRL architecture and analytical tools, while field and laboratory partners generate and validate AMR datasets under real-world conditions. Governance and SSH partners ensure ethical alignment, cultural adaptation, and social uptake. A core innovation of FLAMR is its transparency-by-design, bidirectional community-driven framework, where centralized data informs stewardship and policy intervention through interactive dashboards and geolocated visualizations. Aligned with the Horizon Europe Health Cluster and the EU One Health Action Plan on AMR, FLAMR embodies open science and interdisciplinary capacity-building, creating scalable, equitable pathways to global AMR resilience and long-term EU leadership in responsible One Health AI innovation.

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)

Programme(s)

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Topic(s)

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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.

HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff Exchanges

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2025-SE-01

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Coordinator

EESTI MAAULIKOOL
Net EU contribution

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.

€ 475 950,00
Address
KREUTZWALDI 1
51014 Tartu
Estonia

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Region
Eesti Eesti Lõuna-Eesti
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

No data

Participants (5)

Partners (4)

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