A Brief Investigation about the Current Status of Federated Learning for Health in Australia

Author: Guodong Long, Australian Artificial Intelligence Institute, University of Technology Sydney, Australia

Date: Feb 12, 2026

Federated learning (FL) is a privacy-preserving machine learning paradigm designed to enable collaborative model training across distributed data sources without centralising sensitive data. It has been widely adopted in domains involving personal devices (e.g., smartphones) and data-sensitive institutions, particularly healthcare and finance.

National Infrastructure and Major Investments

In 2023, a A$13.7 million initiative (link), titled NINA – National Infrastructure for Federated Learning in Digital Health, was established and is hosted by the Faculty of Health at The University of Queensland (UQ). Led by Professor Clair Sullivan (UQ), the project received A$6 million from the Medical Research Future Fund (MRFF) under the National Critical Research Infrastructure scheme. This investment reflects growing national commitment to federated learning as a core digital health capability. 

University of Sydney – FLERA

The University of Sydney has established a dedicated research group for federated learning in health: FLERA – Federated Learning Ecosystem for Research in Australia. FLERA operates within the Brain and Mind Centre, a network of medical researchers and institutions focused on neurological and brain-related health conditions.

FLERA is associated with the MRFF-funded project TRANSCEND (A$7 million, link ,2020) — Translating AI Networks to Support Clinical Excellence in Neuro Diseases. This initiative further demonstrates the integration of federated learning into national-scale medical AI research programs.

ARDC National Initiative (2025)

In 2025, the Australian Research Data Commons (ARDC) announced a new project (link) titled Building Secure Federated Machine Learning for Australian Health Research. The project is led by Professor Lois Holloway (UNSW and Ingham Institute) in partnership with:

• Australian Cancer Data Network (UNSW)
• Australian Imaging Service (University of Sydney)
• National Infrastructure for Federated Learning in Digital Health (UQ)

ARDC is Australia’s national digital research infrastructure provider, supporting researchers through advanced data platforms, digital infrastructure, and high-quality data collections. This initiative highlights federated learning as an emerging component of the national research infrastructure strategy.

University of Melbourne – Clinical Applications

Professor Tomas Kalincik (University of Melbourne) published a 2025 paper (link) in NPJ Digital Medicine titled “Personalized Federated Learning for Predicting Disability Progression in Multiple Sclerosis Using Real-World Routine Clinical Data.” This work illustrates the increasing clinical translation of federated learning methodologies into real-world healthcare applications.

University of Technology Sydney - Machine Learning research on FL and its applications

In 2023, a UTS research team led by Guodong Long applied federated learning techniques to social media analysis for detecting medicine shortage events, funded by the Digital Health CRC. The resulting work, “MedShort: A Novel Framework for Medical Document Summarization,” was published at WWW 2025, demonstrating the application of federated learning in public health and digital medicine contexts. Moreover, the team has co-worked with DoH on investigating the future technology framework in "Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health" (link). 

Notes: All information sourced from the Internet. If there is inaccurate or false information, please notify us.