What is Swarm Learning?
Swarm learning is a decentralized machine learning solution that uses edge computing and blockchain technology to enable peer-to-peer collaboration. It allows multiple collaborators to share data insights without sharing the data itself, protecting data privacy and security while allowing all contributors to benefit from collective learnings.
Why is swarm learning important?
As more data is collected and processed at the intelligent edge, its true value can only be realized by sharing it and turning it into a collective understanding. However, sharing data like this introduces security risks and in some cases is prohibited by government regulations. Because swarm learning shares insights rather than source data, the learnings derived from protected data can be safely shared across locations and even across organizations.
AI and swarm learning
Swarm learning can play a vital role in improving the accuracy of artificial intelligence (AI) models. When organizations only have access to their own data, their AI models will evolve based solely on information about those individuals with whom the organization has previously or is currently engaged, creating bias in the models. With swarm learning, an organization can combine its proprietary data with the learnings from other organizations, increasing accuracy and reducing bias.
What are the benefits of swarm learning?
Today, the huge volumes of data generated and collected at different edge locations creates a challenge for a traditional, centralized machine learning approach. These algorithms need data to be in a consolidated location, but moving large amounts of data from multiple sources to a single location introduces security risks and latency concerns.
Swarm learning’s decentralized approach allows data to be applied to AI models closer to the source, with only the learnings being moved. Blockchain technology enables multiple edge locations, collectively called a swarm, to share insights with one another in a trusted manner and prevents bad actors from gaining unauthorized access to the swarm.
This decentralized approach allows models to generate answers more quickly and organizations to have greater opportunities for shared learning, even outside their own four walls. At the same time, the privacy of source data is protected, because data movement is limited. This also reduces data sprawl, as data does not need to be duplicated to a core or central location.
By training models and harnessing insights at the edge, organizations can make decisions more quickly, where they are most relevant, resulting in better outcomes. Dataset sizes available to models can increase, making them more reliable and less prone to biases. At the same time, data governance and privacy are preserved.
HPE and swarm learning
HPE Swarm Learning helps organizations solve the dilemma of massive data growth and the technical, social, and economic challenges of extracting value from data while meeting privacy and ownership requirements. This framework utilizes computing power at or near the distributed data sources to run the ML algorithms that train the models. It uses a blockchain platform to share only the learnings with peers collaboratively, improving insights. Training the model occurs at the edge, where data is most recent, and where prompt, data-driven decisions are necessary.
The blockchain-based security framework employed by HPE Swarm Learning ensures that only legitimate participants join the decentralized learning network and that each party is bound by a smart contract in terms of contribution and rewards. The smart contract in HPE Swarm Learning supports innovative business models, along with a monetization framework, and it also facilitates cross-organization collaboration.
By adopting HPE Swarm Learning, organizations can improve the accuracy and reduce the biases of their AI models and enable a more efficient AI infrastructure. The tool is designed to provide users with containers that are easily integrated with AI models via the HPE Swarm API. AI model learnings can then immediately be shared both inside an organization and with industry peers on a global scale.