Swarm learning – what is it, and why do I need to know about it?

[RichardHatheway]

Swarm learning. It’s the latest in a series of buzzwords that seem to continually appear. Along with artificial intelligence and machine learning, this latest phrase seems to be getting more and more “out there.”

But is it really? Or is it actually a fairly descriptive phrase that talks about a new way to apply technology?

Let’s go deeper and find out…

A short history of swarm learning

The name swarm learning was inspired by the collective behavior of animals and insects that come together to achieve a mutual goal. Think of bees swarming to build a hive, small fish forming a bait ball to scare off larger predator fish, wolves hunting their prey in packs, or birds moving together in flight.

By joining together like that, those insects and animals have pooled their resources and worked together to collectively increase their ability to accomplish a goal. In those examples, the group intelligence has been amplified by coming together so that the performance of the group eclipses the performance of the individual members. Scientists refer to this type of behavior by a variety of terms, including “collective, consensus or swarm intelligence.”

Swarm learning in technology effectively does something similar by harnessing the power of distributed data.

What swarm learning is in technology

In technology, swarm learning is described as “a decentralized machine learning (ML) framework that uses peer-to-peer networking to foster collaboration and blockchain technology to preserve data privacy.” What this means in practice is that swarm learning unites the edge computing capabilities of multiple networked nodes, combined with blockchain technology to allow data exchange and collaboration across a network without violating data privacy requirements.

So how is swarm learning structured differently than traditional machine learning models? Let’s compare.

In traditional machine learning models, a central server hosts a trained model, and then data is fed into that model via a data pipeline. While that process is effective, it is limited by the fact that all the data being evaluated must be sent to and from the central server for processing. That can be a time-consuming and expensive process, as well as being subject to network latency and connectivity issues. To get around those problems, data is often copied, leading to the potential for data duplication errors.

In traditional machine learning models, there’s also the fact that due to data privacy regulations, many datasets cannot be shared or included in the machine learning model structure, so the results may not be as detailed or accurate as they could be. Compare that to the swarm learning model, which is essentially a decentralized machine learning model.

In this type of structure, it’s recognized that data is created at the edge, so this model takes advantage of edge processing capabilities to eliminate sending data back and forth to a central server. Instead, each edge processing location (i.e., node) builds an independent AI model of its own where it can analyze data at the edge, improving efficiency.

In addition, each node is also connected to all the other nodes on the network via peer-to-peer networking, making the network dynamically scalable. This P2P structure also helps eliminate the single point of failure problem, creating a more robust system. And because blockchain technology is used to safeguard the privacy of the datasets, data and learnings can be shared among the networked nodes, regardless of location, providing each node and the model with more data to be analyzed, which improves overall model accuracy and reduces model bias.

As there are now numerous nodes sharing data and working on the same problem together, this structure effectively amplifies the capabilities of the individual nodes on the network. As the models are now more accurate, the overall accuracy of the results is improved in much the same way that animals coming together increase their group intelligence, and therefore, improve the outcome of their actions.

Hence the name swarm learning.

How swarm learning is used

Due to the complexities of today’s AI models, swarm learning is beginning to be used more frequently, especially in industries that generate massive amounts of data, such as healthcare and medical research, financial services, manufacturing, transportation, and logistics. In those industries, being able to rapidly ingest and analyze large quantities of data is critical to improve accuracy and efficiency of models, derive new insights, and improve effective decision making. In the past, however, due to strict data privacy guidelines and regulations, it was often difficult, if not impossible to share data among distributed locales. This is where swarm learning can help.

Because swarm learning leverages blockchain technology to protect data privacy and improve collaboration, swarm learning is fast becoming the default model used to analyze large amounts of data. By making shared data accessible for analysis at edge locations, businesses and organizations can provide better and more data to their AI models, increasing the accuracy and reliability of results. This saves time and enables faster decision making, which in turn leads to better outcomes.

An example of how this could work in the real world is the example of a virus outbreak. In this example, access to relevant data from the very beginning stages of the outbreak is critical to being able to understand the scope and scale of the outbreak. It also allows researchers to begin development of an effective vaccine. Information such as geographic locations of initial outbreaks, patient symptoms, patient contacts, patient travel history, and outcomes of treatments already tried is gathered at each outbreak location.

Because the swarm learning model allows data to be shared without exposing confidential patient information, medical facilities around the world can analyze the data in real time. Immediate access to and analysis of the data results in more accurate models, which can be used to catalog and create a database of symptoms and treatments. This data ultimately helps lead to the development of a vaccine and treatment protocols. Immediate access to the data also allows researchers to create a global map to track the spread of the virus, helping to contain the virus.

As you can see by this simplified example, swarm learning enables a much more efficient and rapid response.

Takeaways about swarm learning

Swarm learning:

• Is a decentralized framework
• Utilizes blockchain technology to safeguard and protect data privacy
• Reduces data duplication and mass data movement, eliminating network latency and connectivity issues
• Increases the efficiency of models
• Enables more accurate model results and outcomes
• Unites edge computing and blockchain-based peer-to-peer networking
• Enables collaboration across distributed nodes using all available data
• Saves time

For more information on swarm learning, visit:

• What’s all the buzz about swarm learning?
• HPE Swarm Learning GitHub library
• HPE Swarm Learning

Hewlett Packard Enterprise

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