Description: Joint Privacy is a fundamental concept in the field of federated learning, focusing on protecting the privacy of data from multiple participants during the training process of artificial intelligence models. This approach allows data to remain on users’ local devices, eliminating the need to centralize it on a server. Joint Privacy is based on the idea that, while individual data is not shared, useful patterns and insights can be extracted from collaboration among different entities. This method not only protects users’ sensitive information but also encourages cooperation among organizations, allowing each to contribute to the development of more robust and accurate models without compromising data security. The main features of Joint Privacy include data anonymization, the use of privacy-preserving learning algorithms, and the implementation of encryption techniques. Its relevance lies in the growing concern over data privacy in an increasingly digital world, where data breaches are common and regulations on information protection are stricter. In summary, Joint Privacy is an innovative approach that enables collaborative learning without sacrificing participant privacy, making it an essential component of federated learning.
