Federated learning is a machine learning technique that allows multiple devices or systems to collaborate in training a centralised model without having to share their sensitive data. In federated learning, devices or systems keep their data private and only share model parameters among themselves.
This technique is useful in situations where sensitive data cannot be shared, for example, when working with medical or financial data. Instead of collecting all data in one place to train a model, federated learning allows data to be kept on local devices or systems, ensuring data privacy.
Federated learning is used in a variety of applications, such as speech recognition, fraud detection and personalised recommendation. For example, in speech recognition, federated learning allows multiple personal devices to participate in training a centralised speech recognition model, which improves the accuracy of the model without compromising the privacy of users' personal data.
Once the basic concepts for building a commercial software with artificial intelligence are clear, where it is defined to whom to dedicate effort and [...]
Read More »The Official Chamber of Commerce of Seville, in collaboration with the Spanish Institute of Financial Analysts (IEAF), offered last March 16th [...]
Read More »The financial sector is constantly implementing new technologies to modernize and digitize its functions. One of the reasons for this is the processing of [...]
Read More »Industry 4.0 or the Fourth Industrial Revolution is based on the integration of digital technologies in the production and processing of goods and services.
Read More »
