Aster DM Healthcare, Intel Corporation and CARPL launches secure federated learning initiative

Federated Learning (FL) is a method of training AI algorithms with data stored at multiple decentralised sources without moving that data

Aster Innovation and Research Centre, the innovation hub of Aster DM Healthcare, has joined with Intel Corporation, and CARPL to announce the launch of ‘Secure Federated Learning Platform.’ This collaboration will enable the development of AI-enabled health tech solutions where data can securely reside where it is generated. The collaboration will boost innovation in areas such as drug discovery, diagnosis, genomics, and predictive healthcare. It will also allow clinical trials to access relevant data sets in a secure and distributed manner.

A single patient generates nearly 80 MB of data annually in imaging and EMR data; according to 2017 estimates, RBC Capital Market projects that “by 2025, the compound annual growth rate of data for healthcare will reach 36 per cent. Genomic data alone is predicted to be 2–40 exabytes by 2025—eclipsing the amount of data acquired by all other technological platforms.”

AI-enabled solutions in areas such as medical imaging are helping to address pressing challenges in healthcare such as staffing shortages and aging populations.  However, accessing silos of relevant data spread across the different hospitals, geographies, and other health systems while complying with regulatory policies is a massive challenge.

Federated Learning (FL) is a method of training AI algorithms with data stored at multiple decentralised sources without moving that data. To facilitate the adoption of federated learning, Intel has led the development of OpenFLopen source framework for training machine learning algorithms,that provide a solution to “data silos” by leveraging Intel’s security technology.

Intel Software Guard Extensions (Intel SGX) offers hardware-based memory protection by isolating specific application code and data in memory. This secure FL solution enables the protection of workload intellectual property (IP) and secures health data with its custodians. OpenFL was combined with CARPL’s rich data extract, transform, and load (ETL) capabilities for end-to-end AI model training.

The success of this pilot has demonstrated engagement to the next level, which is to democratise access to health data across organisational & geographical boundaries without compromising on the data privacy and security aspects.