Hewlett Packard Enterprise Ushers in Next Era in AI Innovation with Swarm Learning Solution Built for the Edge and Distributed Sites - Seite 2

However, sharing data externally may raise a challenge for organizations that are required to meet data governance, regulatory or compliance requirements, mandating that data stay at its location. HPE Swarm Learning uniquely enables organizations to use distributed data at its source, which increases the dataset size for training, to build machine learning models to learn in an equitable way, while preserving data governance and privacy. To ensure that only learnings captured from the edge are shared, and not the data itself, HPE Swarm Learning uses blockchain technology to securely onboard members, dynamically elect a leader, and merge model parameters to provide resilience and security to the swarm network. Additionally, by only sharing the learnings, HPE Swarm Learning allows users to leverage large training datasets, without compromising privacy, and helps remove biases to increase accuracy in models.

“Swarmifying” data to empower AI for the greater good

HPE Swarm Learning can help a range of organizations to collaborate and improve insights:

• Hospitals can derive learnings from imaging records, CT and MRI scans, and gene expression data to be shared from one hospital to another to improve diagnostics of diseases and other ailments, while protecting patient information.
• Banking and financial services can fight the expected global loss of more than $400 billion in credit card fraud over the next decade², by sharing fraud-related learnings with more than one financial institution at a time.
• Manufacturing sites can benefit from predictive maintenance to gain insight into equipment repairing needs and address them before they fail and cause unwanted downtime. By leveraging swarm learning, maintenance managers can gain better insight by collecting learnings from sensor data across multiple manufacturing sites.

Example use cases of early HPE Swarm Learning adopters include:

University of Aachen studies histopathology to accelerate diagnosis of colon cancer

A team of cancer researchers at University Hospital of RWTH University Aachen in Germany conducted a study to advance diagnosis of colon cancer by applying AI on image processing to predict genetic alterations, which can cause cells to become cancerous.

The researchers trained AI models using HPE Swarm Learning on three groups of patients from Ireland, Germany and the U.S. and validated the prediction performance in two independent datasets from the United Kingdom using the same, swarm learning-based AI models. The results demonstrated that the original AI models, training only on local data, were outperformed using swarm learning due to sharing learnings, but not the patient data, with other entities to improve predictions.