Description: Federated learning framework is an architecture that enables the collaborative training of artificial intelligence and machine learning models without the need to centralize data on a single server. This approach is based on the idea that multiple devices or entities can contribute to the learning of a shared model while maintaining the privacy of their data. Instead of sending sensitive data to a central server, each participant trains a model locally and only shares the model parameters, such as weights and biases, which are updated on a central server. This method not only enhances data privacy and security but also reduces latency and bandwidth required to transfer large volumes of data. The main features of the federated learning framework include decentralization, privacy preservation, resource efficiency, and the ability to adapt to different environments and devices. Its relevance has grown in a world where data protection is crucial, especially in sectors such as healthcare, finance, and technology, where personal data is extremely sensitive and must be handled with care.
History: The concept of federated learning was first introduced by Google in 2017 in a paper describing how machine learning models could be trained on mobile devices without compromising user privacy. Since then, it has evolved and been adopted in various applications, especially in the fields of artificial intelligence and data privacy.
Uses: Federated learning is primarily used in applications where data privacy is paramount, such as in healthcare, where patient data cannot be shared. It is also applied in the financial sector to improve fraud detection models without exposing sensitive data. Additionally, it is increasingly used in mobile devices and distributed systems to personalize user experiences while safeguarding personal information.
Examples: An example of federated learning is Google’s text prediction system, which improves its predictive typing model using data from users’ devices without accessing their personal information. Another case is the use of federated learning in healthcare applications, where hospitals can collaborate on improving diagnostics without sharing patient data.
