Description: Local training in the context of Federated Learning refers to the practice of training machine learning models using data that resides on local devices, such as smartphones or personal computers, rather than centralizing that data on a server. This approach allows models to learn from the information available on each device without the need to transfer data to a central server, helping to preserve user privacy and data security. During the local training process, the model is adjusted using the specific data from each device, and only the updated parameters are sent to the central server, where they are combined with parameters from other devices to improve the global model. This methodology not only reduces the need for large volumes of data to be transmitted over the network but also minimizes the risk of exposing sensitive data. Local training is particularly relevant in applications where privacy is crucial, such as in healthcare, finance, and personalized services, as it allows organizations to benefit from data without compromising user confidentiality.<\/p>\n","protected":false},"excerpt":{"rendered":"
Description: Local training in the context of Federated Learning refers to the practice of training machine learning models using data that resides on local devices, such as smartphones or personal computers, rather than centralizing that data on a server. This approach allows models to learn from the information available on each device without the need […]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"glossary-categories":[],"glossary-tags":[],"glossary-languages":[],"class_list":["post-247138","glossary","type-glossary","status-publish","hentry"],"post_title":"Local Training ","post_content":"Description: Local training in the context of Federated Learning refers to the practice of training machine learning models using data that resides on local devices, such as smartphones or personal computers, rather than centralizing that data on a server. This approach allows models to learn from the information available on each device without the need to transfer data to a central server, helping to preserve user privacy and data security. During the local training process, the model is adjusted using the specific data from each device, and only the updated parameters are sent to the central server, where they are combined with parameters from other devices to improve the global model. This methodology not only reduces the need for large volumes of data to be transmitted over the network but also minimizes the risk of exposing sensitive data. Local training is particularly relevant in applications where privacy is crucial, such as in healthcare, finance, and personalized services, as it allows organizations to benefit from data without compromising user confidentiality.","yoast_head":"\n