Description: A Web-Based Framework for Federated Learning is a structure that facilitates the development and deployment of applications that implement federated learning techniques using web technologies. This approach allows multiple devices and systems to collaborate in training artificial intelligence models without the need to share sensitive data, ensuring privacy and data security. The main features of this framework include interoperability between different platforms, scalability to handle large volumes of data, and flexibility to adapt to various network architectures. Additionally, it promotes collaboration among different entities, such as companies and organizations, by allowing each participant to contribute to the global model without compromising the confidentiality of their data. This framework becomes especially relevant in a world where data protection is crucial, and regulations like GDPR require responsible handling of personal information. In summary, a Web-Based Framework for Federated Learning optimizes the training process of machine learning models while establishing a standard for ethics and security in data handling in the digital age.<\/p>\n","protected":false},"excerpt":{"rendered":"
Description: A Web-Based Framework for Federated Learning is a structure that facilitates the development and deployment of applications that implement federated learning techniques using web technologies. This approach allows multiple devices and systems to collaborate in training artificial intelligence models without the need to share sensitive data, ensuring privacy and data security. The main features […]<\/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-316911","glossary","type-glossary","status-publish","hentry"],"post_title":"Web-Based Framework ","post_content":"Description: A Web-Based Framework for Federated Learning is a structure that facilitates the development and deployment of applications that implement federated learning techniques using web technologies. This approach allows multiple devices and systems to collaborate in training artificial intelligence models without the need to share sensitive data, ensuring privacy and data security. The main features of this framework include interoperability between different platforms, scalability to handle large volumes of data, and flexibility to adapt to various network architectures. Additionally, it promotes collaboration among different entities, such as companies and organizations, by allowing each participant to contribute to the global model without compromising the confidentiality of their data. This framework becomes especially relevant in a world where data protection is crucial, and regulations like GDPR require responsible handling of personal information. In summary, a Web-Based Framework for Federated Learning optimizes the training process of machine learning models while establishing a standard for ethics and security in data handling in the digital age.","yoast_head":"\n