Federated Learning
ML approach enabling models to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them.
Federated Learning is significant because it addresses critical issues such as privacy, security, and data access rights by allowing the model to learn from data distributed across various locations without the need to share the data itself. This approach is particularly valuable in scenarios where data cannot be centralized due to privacy concerns, regulatory requirements, or when dealing with large datasets that are impractical to transmit. In Federated Learning, a global model is improved iteratively by aggregating locally computed updates rather than by direct access to the data, thus ensuring that sensitive information does not leave its original location.
The concept of Federated Learning was first introduced by Google in 2016. It gained popularity as a practical solution for training AI models on decentralized data while addressing privacy and data security concerns.
Google has been a pioneer in Federated Learning, with significant contributions from researchers such as Brendan McMahan, who has played a crucial role in the development and popularization of the concept.