Federated Learning: analysing the data, without collecting it
Federated learning is a machine learning technique that allows for training models on decentralized data without the need for data to be centrally collected and aggregated. This means that data can remain on the devices or servers where it is generated and the model can be trained using aggregated gradients from multiple devices.
Traditional machine learning models are typically trained on a centralized server, where data is collected and analyzed to develop a model that can make predictions or identify patterns. However, this approach can be problematic for several reasons, including privacy concerns, the need for large amounts of data, and the cost and complexity of managing centralized servers.
Through Federated Learning you protect data privacy and reduce the risk of data leakage
One of the main benefits of federated learning is the ability to protect data privacy. By keeping data on individual devices, the risk of data leakage is reduced and personal information is less likely to be exposed. This makes federated learning particularly useful in industries where data privacy is a concern, such as healthcare and finance.
Opportunities of a decentralized approach: more accurate and robust models
Another advantage of federated learning is that it can enable training on a much larger and more diverse dataset than would not be possible with a centralized approach. By leveraging the combined data from millions of devices, federated learning enables organizations to train models that are more accurate, diverse, and robust. This leads to better predictions and improved decision-making.
Federated learning has already been applied in a number of areas, including natural language processing, computer vision, and recommendation systems. Some examples of its use include personalizing search results for users, improving the performance of predictive keyboards, and detecting fraudulent transactions.
Federated learning: how it works
To implement federated learning, a central server is typically used to coordinate the training process. The server sends out model updates to participating devices, which then train the model on their local data and send the updated gradients back to the server. The server then aggregates these gradients and uses them to update the global model.
Thanks to this procedure, Federated Learning enables organizations to develop AI models that can be deployed in real-world scenarios where traditional methods may not be feasible. For example, in areas with limited network connectivity, federated learning can enable models to be trai- ned and updated on-device, providing organizations with the ability to deliver AI services even in remote or challenging environments.
By keeping data on the device, Federated learning eliminates the need to transmit sensitive information to a centralized server, reducing the risk of data breaches and other security risks. Additionally, federated learning allows organizations to comply with regulations that restrict the sharing of sensitive information, such as the European Union's General Data Protection Regulation (GDPR).
