Federated Learning – What is it?

Federated Learning

Federated Learning – What is it?

Overview

Machine learning as a concept is nothing new. You come across various examples of machine learning in your day to day lives. Recommendations provided by an ecommerce website is, for example, an outcome of machine learning. Google has been working on a new concept known as Federated Learning which can redefine how computers learn from data. Unlike in the case of machine learning, user data is stored on smartphone apps. The apps learn from user data and inputs over time and become more intuitive. User data is not centralized. It is believed that Federated Learning will result in quicker data processing and more data privacy. This needs to be noted that the concept is new and Google is working on potential problems like excessive load on smartphone processors. Also, it does not seem that Google pioneered or owns the concept. Apple had already worked on this concept, albeit with a different name.








What is Federated Learning?

Federated Learning is a learning process for machines. It is now being specifically applied on smartphones with the Android operating system. The apps on the smartphone learn from user inputs such as keywords and enrich the algorithms. Based on the learning, apps can do a lot of tasks such as provide tailored recommendations. To put the idea into practice, Google is using its own keyboard GBoard on the smartphones for accepting user inputs. It has also implemented a lighter version of its machine learning software Tensorflow on the smartphones. Unlike in machine learning, user data is not stored in the cloud but on the apps. So, user data is more secured and processed quicker.

Must Read – Understanding Machine Learning

Difference between Federated Learning and Machine Learning

The main differences are:

  • Data storage: For machine learning, all user data is stored in the cloud but for federated learning, the user data is stored in the smartphone app.
  • Algorithm: For machine learning, the algorithms are developed or updated based on accumulated user data. For federated learning, custom algorithms are developed based on user data.
  • App update: Machine learning is dependent on app updates while federated learning is not.

Also Read – Influence of machine learning on supply chain management 

How can Federated Learning redefine learning?

Federated Learning promises to improve apps’ capabilities in learning from user inputs and quicker. Apps can offer improved user experience such as better product recommendations.

Limitations

It is believed that high volume data processing will exert a lot of pressure on the smartphone battery and processor.








Conclusion

Federated Learning seems like a promising idea. It can improve how apps adapt to diverse user data and return the benefits. Its key points are better learning abilities and user experience and more data privacy.

Read more interesting articles on Machine Learning 

 

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