Description: Privacy technology refers to tools and systems designed to protect users’ personal data, ensuring that sensitive information is not accessible without proper consent. In the context of federated learning, this technology allows artificial intelligence models to be trained using data distributed across multiple devices or servers, without the need to centralize the information. This means that data remains at its source, reducing the risk of exposure and privacy breaches. Key features of this technology include the ability to perform analysis and learning without compromising data confidentiality, as well as the implementation of algorithms that ensure personal information is not disclosed during the training process. The relevance of privacy technology is increasingly critical in a world where the collection and use of personal data are ubiquitous, and where regulations on data protection, such as GDPR in Europe, require responsible handling of information. In this sense, federated learning presents itself as an innovative solution that allows organizations to benefit from data without compromising individuals’ privacy.
History: The concept of federated learning was first introduced in 2016 by researchers at Google, who sought a way to train machine learning models using data from mobile devices without having to transfer that data to a central server. This approach emerged in response to the growing concern over data privacy and the need to comply with regulations such as GDPR. Since then, federated learning has evolved and been adopted in various applications, from improving prediction models on mobile devices to its use in various sectors to preserve privacy.
Uses: Federated learning is primarily used in applications where data privacy is crucial. This includes sectors such as healthcare, where sensitive data must be protected. It is also applied in the development of artificial intelligence models on various devices, allowing applications to improve their performance without sending personal data to the cloud. Additionally, it is used in the financial industry to detect fraud without compromising customer information.
Examples: An example of federated learning is Google’s text prediction system, which improves its ability to suggest words without collecting user data. Another case is the use of federated learning in medical research, where different institutions can collaborate on training diagnostic models without sharing sensitive data. It has also been implemented in mental health applications, where behavioral patterns are analyzed without compromising user privacy.
