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.
The banking sector has undergone considerable transformations over the past 10 years. Especially as banking has become more integrated and [...]
Read More »The term Business Intelligence (or BI) defines the use of information technologies to identify, discover, and analyze business data, such as business [...]
Read More »The acquisition of new potential customers is one of the most important and difficult processes for a company. Traditionally, it has been necessary to [...]
Read More »Chargeback refers to refunds that occur when, at the request of a cardholder, the bank requests a refund on his or her behalf [...].
Read More »
