Federated learning: What is it?

Federated learning is a decentralized method of training machine learning models (often called collaborative learning). Data exchange from client devices to global servers is not necessary. Rather, the model is trained locally using the raw data on edge devices, hence improving data privacy. The local modifications are aggregated to build the final model in a shared manner.

Here are some reasons why federated learning matters.

  • Privacy: Federated learning allows training to happen locally on the edge device, avoiding potential data breaches, in contrast to traditional approaches that send data to a central server for training.
  • Data security is ensured by sharing only the encrypted model updates with the central server. Furthermore, safe aggregation methods such Only aggregated results can be decrypted according to the Secure Aggregation Principle.
  • Obtaining access to diverse data: Access to data dispersed across numerous devices, regions, and organizations is ensured by federated learning. It allows for the secure and private training of models on sensitive data, like financial or medical data. Moreover, models can be made more generalizable with the use of more diverse data.

How is federated learning implemented? 

At the central server is a generic basic model. The client devices receive copies of this model, and they use the local data they produce to train the models. Individual device models improve with time, becoming more tailored to the user’s needs. 

In the following phase, secure aggregation techniques are used to share the updates (model parameters) from the locally trained models with the central server’s primary model. This model creates new learnings by averaging and combining various inputs. The fact that the data is gathered from several sources increases the likelihood that the model will become broadly applicable. 

The client receives the central model after it has been retrained with the updated parameters devices again for the next iteration. With every cycle, the models gather a varied amount of information and improve further without creating privacy breaches.

Applications of Federated Learning in Real Life

Federated learning is already widely used in numerous industries and application cases. Let’s review some of the most often used ones.

Mobile phones

One of the most popular ways to see federated learning in action is through smartphones. Federated-learning-based solutions include word prediction, facial recognition for logging, and voice recognition when using Google Assistant or Siri. It preserves privacy while assisting in customizing the user experience.


Computer vision and machine learning are used by self-driving cars to assess their environment and interpret their observations in real time. Models must acquire knowledge from a variety of datasets in order to continually adjust to their surroundings and increase precision.

The systems would lag if a conventional cloud-based strategy was used. Federated learning can expedite, accelerate learning and strengthen the models.


What is federated learning, and how does it enhance data privacy? 

Federated learning is a decentralized method of training machine learning models that avoids sending data to a central server. Instead, models are trained locally on edge devices using the raw data on those devices. This approach improves data privacy by keeping the data local and only sharing encrypted model updates with a central server.

How does federated learning ensure data security during model updates? 

Federated learning ensures data security by using secure aggregation techniques. Only encrypted model updates are shared with the central server, and the Secure Aggregation Principle ensures that only aggregated results can be decrypted. This approach protects sensitive data during the training process.

What are some real-world applications of federated learning? 

Federated learning is used in various industries, including mobile phones for word prediction, facial recognition, and voice recognition. It is also applied in transportation, where self-driving cars use federated learning to continuously improve their models based on diverse datasets without causing system lags.

How does federated learning contribute to model generalization? 

Federated learning leverages data from multiple sources, regions, and organizations to train models. This diverse data helps create models that are more generalizable and applicable to a wide range of scenarios. By training on varied datasets, federated learning improves the model’s overall accuracy and adaptability.