Description: Joint Security in the context of Federated Learning refers to the measures implemented to protect both data and learning models in an environment where multiple entities collaborate without directly sharing their data. This approach allows artificial intelligence models to be trained using distributed data, ensuring that sensitive information remains in its original location. The main features of Joint Security include data encryption, the use of anonymization techniques, and the implementation of secure communication protocols. Additionally, it focuses on the integrity and confidentiality of data, ensuring that models are not vulnerable to external attacks or information leaks. The relevance of Joint Security lies in its ability to facilitate collaboration between organizations, allowing for the development of more robust and accurate models without compromising data privacy. This is especially important in sectors such as healthcare, where data is highly sensitive, and in various other sectors where protecting customer information is crucial. In summary, Joint Security is an essential component of Federated Learning, enabling organizations to benefit from collective intelligence without sacrificing data security.
