A Simplified Guide to Federated Learning in AI/ML and How to utilize it

It’s no secret that the global artificial intelligence (AI) market is booming at lightning speed, at a staggering annual growth rate (CAGR) of 38.1%. And with the integration of machine learning (ML) and deep learning, this technology is being increasingly used by AI software development companies. 

Tech giants like Apple and Google are culminating data with machine learning to build thrilling AI models and achieve inconceivable heights of intelligence. One such recent advancement in the AI/ML field is “federated learning.”

What does Federated Learning Mean?

According to AI software development companies, federated learning (FL) is a decentralized form of machine learning to train AI models without letting anyone see or access your data. It helps you unlock information to feed new AI applications and prevent sensitive data, such as Personally Identifiable Information (PII), from getting compromised.

What makes FL special is that it explores different data challenges that come with training at the edge. For instance, training a machine learning or deep learning algorithm would typically require you to centralize all the training data in one place. This may give birth to data privacy and traceability concerns.

However, FL is a decentralized way to model training and make use of the best AI development services. Instead of sending the data to the model, it sends the model to the data, minimizing data privacy concerns, network bandwidth requirements, and data traceability risks.

How does Federated Learning Work?

Using federated learning, multiple people can remotely share their data and train an AI model. This could be done like a team presentation or report. Each party would download the model from a cloud server and then train it on their private data. The updates will be sent back to the cloud and this process may repeat itself until the model is fully trained.

Based on our machine learning solutions company’s observations, federated learning comes in three different types:

  1. Horizontal FL: This is where the central model is trained on similar datasets.
  2. Vertical FL: Data is complementary and combined to predict user preferences.
  3. Federated transfer learning: A pre-trained foundation model is designed to perform one task and is fine-tuned on a different dataset to identify something else.

How to Utilize Federated Learning in AI/ML?

When it comes to making predictions, AI/ML models need tons of training data and the best AI development services. However, companies in heavily regulated industries often feel hesitant to take the risk of sharing sensitive data. For example, the healthcare industry takes privacy laws seriously.

With federated learning, you can overcome such challenges and allow companies to train a shared AI model without sharing confidential records. The technology is particularly useful in scenarios where a huge amount of data is generated and data transfer to a central location is prohibitive. Such scenarios include data generated by self-driving vehicles.

Autonomous driving FL is a great fit for automobiles with Advanced Driver Assistance Systems (ADAS). This is because data volumes are high, network bandwidth is small, and the cars have substantial processing power. You can use the FL technology to train a local model on each vehicle and use the local dataset that the vehicle operation generates.

FL could also help in a wide range of other industries, such as the finance sector. With the help of AI software development companies, you can use the technology to aggregate customer financial records and allow banks to generate more accurate customer credit scores.

In Closing

It seems like federated learning is a new era of safe and secured AI/ML models. It possesses a lot of potential and provides a method to secure sensitive information. Training and testing with FL seem smart, efficient, and sufficient. Although the technology is still in its early stages, it would be interesting to see how it evolves and benefits AL/ML models.

To learn more, contact one of the top AI software development companies.

FAQs:
  1.  Why is FL important now?

AI/ML models are valuable to companies, but traditional centralized approaches have shortcomings like a lack of continual learning and aggregating private data on central servers. These concerns can be alleviated by using FL and enabling continual learning on end-user devices.

  1. Does the FL server need a GPU?

No, you won’t need to have a GPU on the server side. However, certain handlers do need GPUs for the FL server.

  1. How would you help me train my AI model?

As one of the trusted AI software development companies, we will analyze your AI model from different devices and then make necessary updates before sending it back to the cloud. Our team will repeat the process until the model is fully trained.