How Federated Learning Empowers Edge-Computing AI




Wissen Team


May 7, 2024

Over the past couple of years, Artificial Intelligence (AI) has become a mainstream tech used by almost every business that has a digital service used by consumers. Innovations in learning models, natural language processing, etc. have resulted in the deployment of highly accurate AI platforms that can mimic human responses in ways that were never seen before. Generative AI platforms like ChatGPT are the perfect example. For AI systems to work their magic, they must be able to learn continuously from real-world data that is fed to their learning modules.  However, there is a major challenge that is obstructing this ability. Data protection laws and privacy frameworks like GDPR are diminishing the amount of data available in the public domain. It was done to prevent data mishaps and subsequent costs, which according to studies, was USD 4.45 million per data breach in 2023!

The Alternative – Federated learning

AI systems need data to evolve their capabilities regularly to meet market aspirations. However, doing that without obtaining regular data from end users for training is quite impossible. To solve this, AI developers have brought about a paradigm shift in the way their learning models are trained. The introduction of federated learning is making it possible for tech companies to solve the data accessibility problem. Rather than having to bring customer data into their centralized data repository where security threats are more, federated learning allows their learning models to be taken to customer data.

In other words, federated learning is an approach wherein a deep-learning AI model is trained using a collaborative approach. End users are provided with an instance of the learning model that they can execute on their local devices. On the user device, the model is trained with their data until it achieves maturity. The fully trained working model is then shared back to the provider or business. The major benefit of such an approach is that the AI provider doesn’t get access to any end-user data while their learning model is being trained. It doesn’t compromise on data privacy requirements, nor does it miss out on training AI models with the most crucial end-user data needed to evolve.

Edge AI - The Biggest Beneficiary of Federated Learning

Now that you know what federated learning does, it is natural to see its principles aligning with the computational foundations of Edge AI. If you are new to Edge AI, here is a quick summary. Edge AI is an AI system that executes AI-driven operations closer to where the actual user data lies rather than in a centralized server. It is a combination of edge computing and AI, where the key objective is to enable digital services to leverage AI capabilities locally without central cloud connectivity.

The simple explanation highlights underlying similarities between the approach of federated learning and Edge AI. What enterprises must understand is that federated learning can be used to empower Edge AI and help it achieve maximum potential.

Federated Learning Empowering Edge AI

Let us explore four ways in which federated learning can unlock better growth and value from Edge AI applications:

  • Eliminates barriers to information sharing

Federated learning enables Edge AI applications to continuously evolve their understanding of end-user dynamics without the need for taking end-user data to their central cloud storage. This puts end users at an advantage because they do not need to share sensitive data with any business. 

For example, let us consider a weather service powered by Edge AI that can collect data such as the precise location of a user to offer the most accurate weather forecast. In the traditional sense, the network path that facilitates the sharing of location data to a remote server can be a major attack point for cyber criminals to steal and misuse the location information of an end user. Through the federated learning approach, this challenge is eliminated as no data is passed back to a remote cloud server. Instead, the AI app runs on the user’s phone with local AI computing and provides the service.

  • Accelerates performance

Edge AI applications can run from the user’s phone and leverage federated learning approaches to evolve their accuracy and capabilities. This results in a usage scenario where applications can instantly process data, supply insights, and power decisions or controls. Or it could even run in a local data center or computing node closer to the user’s current location if more computing power is needed.

In either case, the federated learning approach ensures that no critical end-user data is shared with any remote server thereby preventing misuse. At the same time, the ability to process data by the AI system locally accelerates the performance of the application significantly.

  • Improves compliance

We have seen the rising challenge involved in meeting regulatory compliance for the usage of user data by AI systems. With a federated learning approach, it becomes easier to enforce this compliance. Data is not centralized and accessed by any 3rd party in such an environment. This model can be a foundation for Edge AI systems to work directly with end-user devices and channels and deliver AI-powered services without the risk of data security breaches.

  • Better accuracy and precision for Edge AI applications

Thanks to federated learning approaches, Edge AI applications can access the right set of end-user data locally and experience learning activities without disruptions. The risk-free availability of diverse training data helps such systems to evolve into very accurate and precise digital assets for both enterprises as well as for customers.

The Bottom Line

Federated learning will be a critical component in the evolutionary life cycle of AI systems. It allows innovations like Edge AI to take off at an accelerated pace and thus equip businesses with more powerful AI services that are refined by actual user data-based learning experiences.

However, the journey into achieving excellence with federated learning, or any AI initiative for that matter, is not without challenges. From skills to knowledge about business impact and possibilities, enterprises need accurate guidance to build the most competent AI systems in their arsenal. This is where a technology partner like Wissen can be of great value. Get in touch with us to know more.