In this world, everything moves in pairs and groups. There are very few examples of “free radicals” in the real world that can be used for evaluation and analysis purposes. The increasing volume of data has shown a remarkable trend in the way data scientists globally are able to extract actionable insights from different kinds of information. In most cases, data scientists would be working with either structured, semi-structured, or unstructured data. It is easy to label these types of data when they come together in groups or cohorts. But, what happens when large data sets of different types of information display more or less the same properties and behaviors? It may not be possible to apply the traditional concepts of Linear Regression, Classification, and Reinforcement Learning to these data sets. In order to understand the behavior of the accumulated cohorts and data sets, data scientists have begun to assemble AI techniques and tools to promote a fascinating domain, called Swarm Learning. In recent months, Swarm Learning has been featured as the most widely followed concept among students pursuing online courses for AI machine learning education.
In this article, we have explained the basic properties of Swarm Learning, the tools used in this domain, and its far reaching applications in specific industries where Swarm Learning algorithms have been found to deliver effective outcomes in a very short span of time.
Let’s start our journey in Swarm Learning for AI courses.
What is Swarm Learning?
Swarm Learning is an advanced application of Artificial Intelligence models blended with existing blockchain frameworks for peer to peer knowledge management. It unifies the different domains in AI such as Machine Learning, Deep Learning, Cognitive Science, Neural networking with blockchain, automation, edge, and fog computing. In order to fully understand the basics of Swarm Learning, you require a certification from a top class online courses for AI and data science that teach decentralized mechanisms in dealing with Big Data using Machine Learning algorithms.
Why Swarm Learning?
There are two important reasons for the growing popularity of Swarm Learning techniques.
Firstly, this platform allows a seamless and transparent alignment of different computing techniques in the AI ecosystem, with decentralized blockchain applications.
Secondly, Swarm Learning also allows AI engineers to maintain data privacy and confidentiality of their machine learning projects without jeopardizing the personal database of participants/ sample population.
Both advantages are directed at ensuring compliance with existing data privacy laws, and also establish a sense of AI ethics in research programs for machine learning data science development that has been debated for a very long time now. With Swarm learning, ML engineers and researchers can regulate their project workflows and outcomes without losing sight of putting data in the wrong hands at any stage of the research.
So, when it comes to sharing the outcomes of the AI research, trainers and developers needn’t share the datasets with the open world. Instead, they can just share the real deal— the decentralized architecture for better knowledge management regulated within the boundaries of data privacy and compliance.
This is exactly what AI leaders have been asking from the machine learning developers for a long time as it allows the proprietary software owners to continue with their business goals without flinching on application development and security management.
Technical Knowledge Required to Head into Swarm Learning
Swarm Learning principles are based on these four pillars of data science:
- AI and machine learning
- Blockchain
- Supercomputing
- Security and risk analysis
In the emerging AI domains, swarm learning is often correlated to another branch of machine learning, called Federated Learning, and this has given rise to a whole new specialization in data science courses. Leading ML courses train algorithms for AI+SL+FL for establishing best practices in knowledge sharing in a secured, blockchain based ecosystem.
If you are willing to pursue training in ML SL, you need to acquire these skills:
- Python / TensorFlow
- Federated Computation
- Keras
- matplotlib, for data visualization
- Linear / Nonlinear regression
- Advanced statistical regression/ classical regression modeling
- Hybrid cuckoo search and singular spectrum analysis (SSA)
- Time series
- Klman filtering technology, and so on.
For blockchain computing, you need to acquire knowledge in these domains:
- Application development
- DeFi architecture and digital ledgers
- Transactional data analysis
- Security and compliance analysis, monitoring, and reporting
Software programming skills include:
- C/ C++
- Ruby
- JavaScript
- Python and R
- Golang
- Cryptography
- Bitcoin and UTXOs
- Smart contracts
Where is Swarm Learning Used?
The biggest market for swarm learning data science is the healthcare business, and that too clinical research domain in it. In clinical research, we are already seeing a huge demand for Python programming based Artificial Intelligence tools for supporting lab test analysis and predictive intelligence. Swarm Learning is a part of the precision medicine research program that has been initiated recently to study the behavior of different types of cells and tissues.
Here are some of the top use case studies related to Swarm Intelligence that have been found to be extremely useful for studies during the online courses for AI data science education.
- Artificial Optimization Algorithms
AI algorithms used in Swarm Learning require a high level of optimization to maximize the efficiency of the automation and computing. These could be done using two types of methods called approximate and exact computing algorithm development.
There are different types of optimization available for the trainers, such as Dynamic Optimization and Mathematical Optimization.
- Support Vector Regression (SVR)
Swarm learning techniques use SVR for class separation. It could be used to define hyperplanes in the data set and formalize the scaling of any data set using different types of kernels, including Gaussian.
SVR and machine learning training concepts are used heavily in defining the scope of load forecasting in trend analysis, for example of cuckoo search and other hybridized ML models.
- Precision Medicine
Patients seek 100% accuracy when they are going to doctors and with technology in place, the expectations rise naturally. In lieu of the applications of machine learning in medicine and diagnostics, Swarm Learning can be used to detect the extent of diseases that heterogeneously infect or affect certain specific groups or groups of patients. For example, COVID-19 among healthcare workers or bone cancer among engineers working in nuclear plants and radiology departments, or breast cancer among women in the age group of 15 and 39 years with the previous case of cancer in their families. Using precision medicine and personalized healthcare developed using AI bots and image recognition software, it is quite possible to extend the best possible treatment to the patients. Swarm Learning ensures that none of the patient healthcare data, one of the costliest data sources, falls into the hands of miscreants or lands on the dark web. It is absolutely safe as far as security is concerned, guaranteed by Swarm Learning developers who work day and night to ensure transparency, confidentiality, and accuracy between patients and AI users.
Like any technology, Swarm Learning too has a few challenges, but these are mostly restricted to testing and training data in the silo. With the rise of Swarm Learning in clinical research programs, we should expect further growth of machine learning algorithms specifically designed with Blockchain features.
