Description: Joint Communication in the context of federated learning refers to the exchange of information among multiple participants collaborating to improve the training of an artificial intelligence model without the need to share sensitive data. This approach allows different entities, such as educational institutions or companies, to contribute to the creation of more robust and accurate models while maintaining the privacy of their data. Communication occurs through the transmission of model parameters, such as weights and biases, rather than raw data. This not only protects data privacy but also reduces the need to transfer large volumes of information, which is especially relevant in high-speed networks like 5G. The ability of 5G networks to provide low latency and high data transmission capacity facilitates the implementation of joint communication, allowing models to be updated in real-time and quickly adapt to new information. In summary, Joint Communication is an essential component of federated learning, combining collaboration among multiple parties with data protection, thus optimizing the training process of artificial intelligence models.
