Description: Local model updating is a fundamental process in the realm of federated learning, where the goal is to enhance an artificial intelligence or machine learning model using data that resides on local devices, without the need to centralize the information. This approach allows each device, such as a smartphone, tablet, or computer, to train a model locally with its own data, resulting in a personalization and adaptation of the model to the specific characteristics of each user or environment. The update is carried out by incorporating new local data, enabling the model to evolve and improve its accuracy and performance over time. This process is crucial for maintaining data privacy, as it avoids the transfer of sensitive information to central servers, aligning with data protection regulations. Furthermore, local model updating contributes to the efficiency of learning, as it reduces the need for large volumes of centralized data and allows for faster and more effective learning. In summary, local model updating is an essential component of federated learning, combining continuous model improvement with user privacy protection.
