Hewlett Packard Enterprise (HPE), has today announced the launch of HPE Swarm Learning, an AI-powered privacy-preserving, decentralized machine learning solution that allows users to share learnings or insights at edge, or distributed sites without compromising data privacy.
HPE Swarm Learning was developed by Hewlett Packard Labs, the company’s R&D organization. According to HPE, this solution provides customers with containers that are easily integrated with AI models using the HPE swarm API, then enabling them to share AI model learnings or insights 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.”
To address data governance, regulatory or compliance requirements concerns, 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. Thereby, ensuring that only learnings captured from the edge are shared, and not the data itself.
HPE also announced today that it is removing barriers for enterprises to easily build and train machine learning models at scale as well as realize value faster, using the new HPE Machine Learning Development System. The HPE Machine Learning Development System is a purpose-built end-to-end solution for AI that integrates a machine learning software platform, compute, accelerators, and networking to develop and train more accurate AI models faster, and at scale.