Federated transfer learning is a term from the fields of artificial intelligence and big data, primarily used in the development of smart, data-driven applications. In traditional machine learning, huge volumes of data are collected and processed centrally. Federated transfer learning takes a different approach: the data remains where it is generated \u2013 for example, on smartphones or within various companies \u2013 and is not sent to a central location. Instead, the artificial intelligences \u201elearn\u201c locally and only share the results or models with each other.<\/p>\n
The concept of \u201etransfer learning\u201c means that an AI model learns from specific experiences or datasets and applies this knowledge to new, similar tasks. Federated transfer learning combines both, ensuring that multiple different sources benefit from each other without sharing sensitive data.<\/p>\n
A clear example: Several hospitals want to train an AI together to detect diseases earlier. However, they cannot share their patient data due to data protection reasons. Federated transfer learning allows each hospital to learn locally and only share learning outcomes. This way, everyone benefits from the exchange without revealing private data.<\/p>","protected":false},"excerpt":{"rendered":"