Dr. Dom Gorse1, Peter Marendy1, Diego Guillen1, Dr. Rebekah Eden2, Andrew Jones1, Nicole Mc Donald2, Dr. Yasmeen George3, A/Professor Xingliang Yuan4, Professor Steven M McPhail5, Professor Leonie Callaway6, Dr. Gnana Bharathy7, Professor Jason Ferris2, Professor Clair Sullivan2
1Queensland Cyber infrastructure Foundation (QCIF), Australia, 2The University of Queensland, Australia, 3Monash University, Australia, 4The University of Melbourne, Australia, 5Queensland University of Technology, Australia, 6Queensland Health, Australia, 7Australian Research Data Commons (ARDC), Australia
Biography:
Dom Gorse is a Data Scientist with more than 30 years’ experience in software development, information management, data mining and data modelling applied to life science and health.
He is the Director of QCIF Data Science and Head of QCIF Bioinformatics, where he provides technical, operational and management leadership to ensure successful delivery of customised analytics solutions to unlock the full value of large-scale biological and clinical data sets.
Abstract:
Federated analytics represents a paradigm shift in the field of data processing and machine learning, offering a decentralised approach that prioritises privacy and control over data. Each participating site independently processes its locally stored data to answer database queries, generate descriptive statistics, or train machine learning models. The key advantage is that these sites only share the results of their computations which are then aggregated to derive global insights or form a global model. As AI-driven research becomes increasingly prevalent, federated learning is garnering significant attention.
While federated learning offers significant benefits, it also introduces unique challenges. These challenges include the need for specific skills and robust infrastructure at each participating site, navigating uncharted territory for data governance, and prompting a shift in the governance model of trusted research environments. Addressing these challenges is crucial for the successful implementation and adoption of federated analytics and requires a collaborative approach between servers, platforms, and sites.
The National Infrastructure for Federated Learning in Digital Health to Generate New Models of Care for Chronic Diseases (NINA) project is a pioneering initiative in the field of digital health research. It leverages federated learning to address the critical unmet need of chronic diseases. With over 20 partners across Australia, NINA aims to establish a national capability and infrastructure network for enabling federated learning in Australia.
This presentation provides an overview of the NINA project, discuss the benefits and challenges of federated learning, and present the strategies adopted by NINA to overcome these challenges.