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Federated learning

What is federated learning?

Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to one server, as well as to more classical decentralized approaches which often assume that local data samples are identically distributed.

Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus allowing to address critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. Its applications are spread over a number of industries including defense, telecommunications, IoT, and pharmaceutics.

How does federated learning work?

Let’s take an example. Say you got selected as a machine learning intern in a company, and your task is to create a robust machine learning application, that needs to train itself on user-sensitive data.

You’re allowed to extract user data, aggregate it from many users, and stack them up on a centralized cloud server, for your model to crunch it. But it might infringe on someone’s privacy. 

You discussed it in a meeting, and your boss is now worried about the next steps. In the meantime, you and your teammates started discussing the matter.

One of them yelled, “What if we don’t take user-sensitive data, but train our model locally, on each device?”

We will train our model on the devices themselves, and not on the centralized server, that exposes sensitive data!  The local data generated by the user history, on a particular device, will now be used as on-device data to train our model and make it smarter, much quicker.

The pipeline goes as follows: 

  • So, our centralized machine learning application will have a local copy on all devices, where users can use them according to our needs.
  • The model will now gradually learn and train itself on the information inputted by the user and become smarter, time to time.
  • The devices are then allowed to transfer the training results, from the local copy of the machine learning app, back to the central server.
  • This same process happens across several devices, that have a local copy of the application. The results will be aggregated together in the centralized server, this time without user data.
  • The centralized cloud server now updates its central machine learning model from the aggregated training results, which is now far better than the previously deployed version.
  • The development team now updates the model to a newer version, and users update the application with the smarter model, created from their own data!

What are the benefits of federated learning?

Here are some primary benefits of federated machine learning:

  • FL enables devices like mobile phones to collaboratively learn a shared prediction model while keeping the training data on the device instead of requiring the data to be uploaded and stored on a central server.
  • Moves model training to the edge, namely devices such as smartphones, tablets, IoT, or even “organizations” like hospitals that are required to operate under strict privacy constraints. Having personal data remain local is a strong security benefit.
  • Makes real-time prediction possible, since prediction happens on the device itself. FL reduces the time lag that occurs due to transmitting raw data back to a central server and then shipping the results back to the device.
  • Since the models reside on the device, the prediction process works even when there is no internet connectivity.
  • FL reduces the amount of hardware infrastructure required. FL uses minimal hardware and what is available in mobile devices is more than enough to run the FL models.


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Federated learning

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is federated learning?

Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to one server, as well as to more classical decentralized approaches which often assume that local data samples are identically distributed.

Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus allowing to address critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. Its applications are spread over a number of industries including defense, telecommunications, IoT, and pharmaceutics.

How does federated learning work?

Let’s take an example. Say you got selected as a machine learning intern in a company, and your task is to create a robust machine learning application, that needs to train itself on user-sensitive data.

You’re allowed to extract user data, aggregate it from many users, and stack them up on a centralized cloud server, for your model to crunch it. But it might infringe on someone’s privacy. 

You discussed it in a meeting, and your boss is now worried about the next steps. In the meantime, you and your teammates started discussing the matter.

One of them yelled, “What if we don’t take user-sensitive data, but train our model locally, on each device?”

We will train our model on the devices themselves, and not on the centralized server, that exposes sensitive data!  The local data generated by the user history, on a particular device, will now be used as on-device data to train our model and make it smarter, much quicker.

The pipeline goes as follows: 

  • So, our centralized machine learning application will have a local copy on all devices, where users can use them according to our needs.
  • The model will now gradually learn and train itself on the information inputted by the user and become smarter, time to time.
  • The devices are then allowed to transfer the training results, from the local copy of the machine learning app, back to the central server.
  • This same process happens across several devices, that have a local copy of the application. The results will be aggregated together in the centralized server, this time without user data.
  • The centralized cloud server now updates its central machine learning model from the aggregated training results, which is now far better than the previously deployed version.
  • The development team now updates the model to a newer version, and users update the application with the smarter model, created from their own data!

What are the benefits of federated learning?

Here are some primary benefits of federated machine learning:

  • FL enables devices like mobile phones to collaboratively learn a shared prediction model while keeping the training data on the device instead of requiring the data to be uploaded and stored on a central server.
  • Moves model training to the edge, namely devices such as smartphones, tablets, IoT, or even “organizations” like hospitals that are required to operate under strict privacy constraints. Having personal data remain local is a strong security benefit.
  • Makes real-time prediction possible, since prediction happens on the device itself. FL reduces the time lag that occurs due to transmitting raw data back to a central server and then shipping the results back to the device.
  • Since the models reside on the device, the prediction process works even when there is no internet connectivity.
  • FL reduces the amount of hardware infrastructure required. FL uses minimal hardware and what is available in mobile devices is more than enough to run the FL models.


Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

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