MULTIPLi Health - MULTIcentric and Privacy-preserving Learning in Healthcare

The increasing availability of heterogeneous healthcare data across institutions creates unprecedented opportunities for data-driven medicine, while simultaneously raising critical challenges related to privacy, governance, and data sharing. As a result, many high-value healthcare datasets remain isolated across institutions, limiting the development, validation, and generalizability of Artificial Intelligence (AI) models for clinical and biomedical applications, despite multicentric studies being widely regarded as the gold standard for robust and generalizable medical evidence. Multicentric and privacy-preserving learning paradigms, such as Federated and Distributed Learning, offer a promising path forward by enabling collaborative model training and analysis without requiring the exchange of raw and possibly sensitive patient data.

The MULTIPLi Health workshop addresses the methodological, technical, and practical challenges associated with building trustworthy AI systems under these constraints. It focuses on learning from multicentric and heterogeneous healthcare data while ensuring compliance with privacy regulations. The workshop aims to examine recent advances as well as unresolved issues related to scalability, robustness, evaluation, and deployment in real-world clinical environments.

By aligning with AIME’s focus on medical decision support and clinically meaningful evaluation, MULTIPLi Health provides a forum for advancing privacy-aware AI methodologies that are essential for the next generation of collaborative healthcare research.

Workshop schedule

Coming soon...

Topics and themes

Topics of interest include, but are not limited to:

• Federated learning methods and architectures for healthcare applications

• Federated analytics and distributed statistical analysis across hospitals and cohorts

• Distributed and collaborative learning under data heterogeneity and imbalance

• Privacy-preserving machine learning (e.g., differential privacy, secure aggregation, secure multi-party computation)

• Learning from multicentric, multi-modal, and longitudinal healthcare datasets 

• Evaluation, benchmarking, and reproducibility in federated and multicentric settings

• System infrastructures, platforms, and frameworks for distributed healthcare AI

• Clinical use cases and real-world deployments of federated and privacy-preserving learning

• Trust, robustness, and fairness in privacy-aware collaborative models

• Explainable AI in federated and distributed learning

Call for papers

Important Dates

• Submission Due Date: Friday, May 22nd, 2026, AoE

• Notification of Acceptance: Friday, June 5th, 2026, AoE

• Workshop Date: Friday, July 10th, 2026

Submission Instructions

We accept submissions of the following formats:

• Short papers (4-6 pages)

• Long papers (up to 10 pages)

Submitted papers should be formatted according to Springer's LNCS Format.

Submissions are processed via EasyChair.
All submissions will be peer-reviewed by at least two members of the Program Committee.

Workshop Proceedings

Publication of accepted papers is optional. Preferences can be indicated after acceptance.
We intend to offer publication through CEUR Workshop Proceedings.

Presentation Instructions

Accepted papers have to be presented in-person.
Participants have to register through the Conference registration page.
More information will be provided soon.