HPE Swarm Learning Software 10 Containers 1-year Subscription E-LTU
Are you concerned about data privacy or regulations when moving data for your machine learning (ML)? Is your data distributed and preventing you from collaboration for improved insights where they are most needed? HPE Swarm Learning provides decentralized privacy-preserving, edge ML at the data source. The blockchain network provides the ability to collaboratively share the learnings of the models with participating HPE Swarm Learning nodes for insights at the data source, tremendously enhancing data privacy and improving insights.
HPE Swarm Learning extends federated learning and obviates the need for a central server. A decentralized, privacy-preserving ML framework utilizes the computing power at, or near, the distributed data sources to run the ML algorithms that train the models. Training the model occurs at the edge where data is most recent, where accurate, and data-driven decisions are necessary.
SKU # R8S86AAE
- Fully-supported containerized software.
- Three software SKU’s are available 5, 10 and 20 nodes with a 1-year subscription license.
Preserve Privacy for Machine Learning
With HPE Swarm Learning, raw data is not transferred to a central location or between locations: source data stays at the data source.
The learnings are shared between participating nodes, preserving data privacy, and improving insights.
Decentralized Machine Learning
HPE Swarm Learning unlocks machine learning with features like global-state merge, without needing a centralized node for training.
Collaborative model training at edge devices.
Parameters are merged at the edge or data source.
Decentralized architecture increases reliability: there is no single point of failure.
Machine Learning at the Edge Where Data Resides
HPE Swarm Learning preserves network bandwidth, as learnings are at the data source.
Near or at data source enables prompt inferences.
Improved Efficiency for Model Training
In case of failure, HPE Swarm Learning allows the remaining nodes to continue machine learning. As the node comes up, it continues participation.
No back-and-forth transfer of data, saving bandwidth and data duplication.
Enables prompt inferences at the data source.
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