New HEREDITARY results bring Europe closer to secure, federated AI for health research

by Admin | Jul 10, 2026 | Hereditary

The HEREDITARY project has reached another important milestone in its third year by delivering a new set of scientific and technical results to the European Commission. At Month 30, the consortium has completed six key deliverables that reinforce the project’s vision of enabling secure, privacy-preserving and multimodal research on neurodegenerative diseases across Europe.

These results represent significant progress across several work packages, ranging from clinical use cases and federated infrastructures to artificial intelligence, legal and ethical frameworks, and privacy-preserving analytics.

From data preparation to federated multimodal ALS research

One of the most relevant achievements is the publication of D2.17: Neurodegenerative Use Cases: Intermediate Results, led by the University of Torino (UNITO). This deliverable marks an important step towards federated multimodal research on neurodegenerative diseases. Using Amyotrophic Lateral Sclerosis (ALS) as its main demonstrator, the deliverable shows how HEREDITARY is moving from use-case design to operational data readiness, integrating FAIRified datasets, semantic interoperability through the HERO ontology, privacy-preserving genomic discovery, and biological insights into a common framework for federated research. Another important aspect of this work is the systematic inclusion of sex-disaggregated analyses, helping identify biological differences that may influence disease progression, diagnosis or treatment response.

The deliverable also outlines the project’s roadmap for ontology-enabled multimodal patient stratification, combining advanced machine learning and semantic technologies to support future federated analyses across institutions. In doing so, it validates Milestone 9 and establishes the scientific and technical foundations for the next phase of HEREDITARY’s neurodegenerative use cases.

Building trustworthy federated AI through privacy-preserving analytics

The newly released D3.8: Privacy-preserving Analytics: First Release, coordinated by Aalborg University (AAU), presents the first implementation of the privacy-preserving technologies that will be integrated into HEREDITARY’s federated analytics and machine learning platform.

The report describes how multiple complementary privacy technologies, including differential privacy, secure aggregation and homomorphic encryption, can be combined to protect sensitive information while still enabling collaborative data analysis across institutions. Beyond describing the underlying algorithms, the deliverable introduces mechanisms to continuously monitor privacy risks during federated computations and evaluates how different protection levels can be adapted depending on the analytical workflow.

These developments establish the privacy layer that will support future federated AI models and formally achieve Milestone MS10, dedicated to privacy-preserving methods within HEREDITARY.

Combining biomedical data for more accurate AI models

The publication of D4.5: Multimodal Learning Methods, led by Radboud University Medical Center (RUMC), presents a new set of computational tools and machine learning methods for multimodal biomedical analysis focused on gut health.

The deliverable also evaluates different strategies for combining histopathology, microbiome and clinical data. The results show that integrating histopathology with fecal microbiome data provides the strongest predictive performance, establishing an important methodological baseline for future multimodal analyses within HEREDITARY. The deliverable also foresees the incremental publication of trained models through Grand Challenge platforms and as open-source software, supporting transparency, reproducibility and collaboration within the scientific community.

Strengthening ethical and legal foundations for European health data sharing

The consortium has released D7.2: In-depth Legal and Ethical Study, coordinated by KU Leuven, which expands the initial legal and ethical inventory developed earlier in the project.

The report analyses the regulatory challenges associated with federated research infrastructures, including GDPR compliance, secondary use of health data, governance responsibilities and cross-organisational collaboration. Rather than providing only theoretical guidance, the study evaluates these issues across HEREDITARY’s clinical use cases, offering practical recommendations for implementing secure “tool-over-data” approaches where algorithms travel to the data instead of transferring sensitive datasets.

Complementing the legal study, the consortium has also published the updated D2.2: Ethical Guidelines, Data Collection and Sharing. The document revises the project’s ethical guidance based on the experience gained during the first half of the project and reflects the evolving requirements for federated model training and multicentre collaboration. It updates recommendations for data sharing, incorporates revised procedures for prospective data collection where necessary and strengthens the links between ethical governance activities and the project’s wider regulatory framework.

Federated infrastructure Implementation

In parallel, the consortium has successfully submitted D2.12: Federated Infrastructure Implementation, marking another important technical achievement for the project. This deliverable advances the implementation of HEREDITARY’s federated infrastructure, which enables secure distributed analysis across participating institutions without centralising sensitive data. Although the deliverable has been officially delivered to the European Commission, it is not yet publicly available because parts of its content are currently under scientific review and publication.

Looking ahead

These new deliverables illustrate how HEREDITARY continues to transform its scientific vision into practical technologies and validated methodologies.

By simultaneously advancing federated infrastructures, privacy-preserving AI, multimodal machine learning, integrated clinical research and trustworthy governance frameworks, the project is steadily building an ecosystem where researchers can collaborate across Europe while keeping sensitive health data protected. As the project moves towards its next phase, these achievements provide a solid foundation for the forthcoming demonstrations, validation activities and clinical applications that will further accelerate innovation in neurodegenerative disease research.