Description: Whitening is a data preprocessing technique used to transform datasets into a form where features are uncorrelated and have a variance of one. This process is fundamental in the fields of machine learning and artificial intelligence, as it enhances model effectiveness by eliminating redundancies and facilitating convergence during training. Whitening is based on the idea that data should be normalized and decomposed into orthogonal components, meaning each feature becomes independent of the others. This is achieved through mathematical techniques such as Singular Value Decomposition (SVD) or the Karhunen-Loève transformation. By applying whitening, the aim is not only to improve the accuracy of predictive models but also to reduce computation time and optimize overall system performance. In the context of federated learning, where data is distributed across multiple devices, whitening can be crucial to ensure that models learn effectively without compromising data privacy. In summary, whitening is an essential tool in data preprocessing that allows machine learning algorithms to operate more efficiently and effectively.