Hewlett Packard Enterprise

Geneva, Switzerland

Hewlett Packard Enterprize and Inartis created a startup challenge and conference on AI & IoT. Following the digital reset introduced by the COVID-19 pandemic, the companies presented their contribution to the emergence of a new digital world. The keynote session was by Dr. Goh on "Uniting the Hospitals in the World by Secured Blockchain for AI & Collaboration", which was followed by a panel discussion and networking lunch at the HPE Innovation Center in Geneva.

The panelists of the Digital Transformation conference in Geneva

I got introduced to Dr. Goh, senior vice president AI HPE, who gave a presentation on Private Permission Blockchain. HPE have created something for the first time that will be critical for healthcare - a neural network training for local instances (e.g., hospitals) to operate on top of their data, and share only insights, not data, so that AI can work across hospitals, organizations, systems, and countries. So, zero data is exchanged while the global algorithm is training on all instances, thus providing the best predictiveness of disease progression. Dr. Goh's use-cases were vaccine/drug side-effects, drug discovery, disease progression, genomics, etc.

Dr. Goh giving a presentation on Swarm Learning

I also got introduced to Anil Soni, the CEO of the WHO Foundation. It seems a great partner to propagate swarm learning. For a private permission blockchain, one needs a "notary", i.e., someone that gives "permission" to start that blockchain process, and the WHO would be an ideal notary. So, the HPE-WHO partnership would solve two problems: 1) data ownership and privacy, and 2) the mandate and eligibility of healthcare systems to share data insights for AI training purposes. This will have far-reaching consequences for healthcare, as we can expect global initiatives respecting local restrictions and regulations.

Swarm Learning as a way to unite hospitals of the World

The authors have published on swarm learning in Nature, and here is the abstract: "Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine."