“Give a man a fish, and you feed him for a day; teach a man to fish, and you feed him for a lifetime.”
This wise saying has survived through the centuries and hasn’t lost a single pinch of relevance. Learning is the core process that keeps the human brain young and active, being able to take up and master any new mental activity.
Don’t look at these lines with such bewilderment in your eyes. Take a look at the very first line of this article and replace the word “man” with “machine” (we all know that the saying is not literally about the fishing). Yes, machines can learn, too.
Artificial intelligence and machine learning need no explanation. They are clear from what you just read. Instead of engineering or coding every single aspect of a technology’s behavior, it’s enough to set up the learning and decision-making process. Yes, it’s just like the man from the saying.
We Use AI Every Day Already. But...
We use AI every day, even if we don’t have any intention of doing so. The almighty Google is probably the best example. It decides on its own what to show according to this or that query, analyzing tons of various data.
It is impossible to train large-scale deep learning networks without millions of images and other types of labeled data. In other words, companies with large amounts of data can create various types of predictive models to “control,” such as the abovementioned Google or Facebook, with its 50 million pieces of human data sold to Cambridge Analytica, which affected the American presidential election.
In recent years, many countries have frequently created policies related to data security and privacy protection. In the future, the use of data will become more cautious. At the same time, data interaction and data fusion are clearly becoming a global trend, but due to industry competition, privacy, security, data ownership, and complicated administrative procedures, data cannot be effectively applied because it’s hard to integrate. CyberVein Federated Learning concept aims to solve this problem.
Can Big Data Be Privacy And Security Oriented, And Satisfy Regulatory Requirements At The Same Time? Enter: CyberVein.
CyberVein has been offering big-data management solutions for quite a while to help users understand that data privacy and ML can coexist without any dissonance. The key features of CyberVein Federated Learning are the following:
- All data is kept locally without compromising privacy or violating regulations.
- Multiple participants join locally trained models to establish a shared virtual model and a system of common benefits.
- Each participant runs a local DAI client, and each client is a decentralized node on the chain to participate in federated learning.
- The communication generated by the platform passes through CyberVein to ensure that all information in the entire system can be traced and priced.
- The system integrates federated learning algorithms, both traditional and new ones.
Learn more about CyberVein here.
What Does CyberVein Federated Learning Actually Solve?
CyberVein Federated Learning is much like a house’s intelligent access control system, which guarantees the security of “properties” in the room. CyberVein prevents web crawlers from selling data of various enterprises so that AI models can be trained safely and effectively without forcing the data to leave the local server.
An efficient machine learning model training allows meaningful data to be professionally processed to gain knowledge, business opportunities, and higher levels of convenience.
You can check CyberVein out here: https://bit.ly/2X3XwFD
Medical Industry: How to BringMachine Learning to a New Level?
If we look at how and where to implement this solution, the answer will be found on the surface. The medical industry operates with great amounts of big data, which must be kept safe. The hidden value in medical big-data is a treasure to be discovered. The data has to be mastered and stored effectively to improve the quality of data modeling, and that requires deep processing and analysis.
CyberVein proposes a federated learning workflow for medical research that can effectively transfer the knowledge learned by the model to different distributed data, ensuring the federated learning performance. Knowledge distillation is used to effectively reduce the gradient of transmission and save huge communication overhead. The decentralized features of the blockchain and the federal learning algorithm effectively protect the security of communications and computing. Whether it is static or dynamic data, CyberVein Federated Learning offers a machine learning framework that uses data more efficiently and accurately while satisfying data privacy, security, and regulatory requirements.
This knowledge has potential value for improving medical quality, effectively controlling expenses, and ensuring medical safety. The platform can improve diagnosis and treatment, assist hospital management personnel in decision-making, and accelerate the implementation of scientific research results.