Introduction to Federated Learning

Federated learning is an innovative machine learning technique that enables the training of models using private data on users' devices, without aggregating data centrally. This approach addresses privacy concerns while still allowing valuable data to be utilized. The technique distributes computations to the user's device, reducing the need for central server resources. However, it poses several challenges, such as data skewness and model aggregation. As more users value privacy, federated learning is receiving increased attention for its potential in preserving data confidentiality.