Description: Local Update in the context of federated learning refers to a process where a machine learning model is trained using data residing on a local device, such as a mobile phone or personal computer. This approach allows the model to improve its performance based on user-specific or local environmental information without the need to transfer data to a central server. The update is performed in such a way that only the adjusted model parameters are sent, rather than the data itself, ensuring the privacy and security of the information. This method is particularly relevant in scenarios where data protection is critical, such as in healthcare or finance applications. Additionally, Local Update contributes to the efficiency of learning, as it allows models to quickly adapt to variations in local data, thereby improving their accuracy and relevance. In summary, Local Update is an essential component of federated learning that enables the personalization and continuous improvement of artificial intelligence models, all while preserving user privacy.
History: The concept of Local Update originated with the development of federated learning, which was first proposed by Google in 2017. This approach emerged as a solution to the privacy and data security issues in training machine learning models. As concerns about data protection grew, federated learning and Local Update became increasingly relevant, especially in sectors such as healthcare and finance, where sensitive data must be handled with care.
Uses: Local Update is primarily used in machine learning applications where data privacy is paramount. For example, in healthcare applications, models can be trained on patient data without personal information leaving the user’s device. It is also applied in the development of intelligent systems, where the model can learn from user interactions without compromising personal information.
Examples: An example of Local Update can be seen in the predictive text system of devices, where the model adapts to the user’s writing preferences without sending their messages to a server. Another case is the training of voice recognition models on personal devices, which learn from local voices without storing recordings in the cloud.
