What is the primary advantage of federated learning?

The primary advantage of federated learning is its ability to ensure data privacy and security for users. In this approach, instead of transferring raw data to a centralized location for training, the training process occurs directly on the devices where the data resides. This means that sensitive information remains on user devices, reducing the risk of data breaches and unauthorized access. By aggregating model updates rather than data itself, federated learning maintains user privacy while still enabling the model to learn effectively from diverse datasets.

Furthermore, this method aligns well with regulations and privacy laws that require user consent for data use, as it minimizes data handling and sharing. The process allows organizations to develop AI models without compromising personal data, making it a significant advancement in ethical AI practices.