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

Hewlett Packard Enterprise (NYSE: HPE) today announced the launch of HPE Swarm Learning, a breakthrough AI solution to accelerate insights at the edge, from diagnosing diseases to detecting credit card fraud, by sharing and unifying AI model learnings without compromising data privacy.

HPE Swarm Learning, which was developed by Hewlett Packard Labs, HPE’s R&D organization, is the industry’s first privacy-preserving, decentralized machine learning framework for the edge or distributed sites.¹ The solution provides customers with containers that are easily integrated with AI models using the HPE swarm API. Users can then immediately share AI model learnings within their organization and outside with industry peers to improve training, without sharing actual data.

“Swarm learning is a new, powerful approach to AI that has already made progress in addressing global challenges such as advancing patient healthcare and improving anomaly detection that aid efforts in fraud detection and predictive maintenance,” said Justin Hotard, executive vice president and general manager, HPC & AI, at HPE. “HPE is contributing to the swarm learning movement in a meaningful way by delivering an enterprise-class solution that uniquely enables organizations to collaborate, innovate, and accelerate the power of AI models, while preserving each organization’s ethics, data privacy, and governance standards.”

VIDEO: The Big Shift: What is Swarm Learning?

Introducing a new AI approach to securely harness insights at the edge

Today, the majority of AI model training occurs at a central location, which relies on centralized merged datasets. However, this approach can be inefficient and costly due to having to move large volumes of data back to the same source. It can also be constrained by data privacy and data ownership rules and regulations that limit data sharing and movement, which can potentially lead to inaccurate and biased models. By training models and harnessing insights at the edge, businesses can make decisions faster, at the point of impact, leading to better experiences and outcomes. Additionally, by sharing learnings from one organization to another at the data source, various industries across the world can unite and further improve intelligence that can lead to tremendous business and societal outcomes.