Description: Distributed Learning is a machine learning approach where data is distributed across multiple machines, allowing models to be trained collaboratively without the need to centralize information. This method is particularly relevant in contexts where data privacy and security are paramount, as it enables organizations to maintain control over their sensitive data. Instead of sending data to a central server, each device or node trains a local model and only shares the model parameters, reducing the risk of data exposure. This approach is supported by techniques such as Federated Learning, which allows multiple devices and systems to perform machine learning without compromising user privacy. Additionally, it can be integrated with recurrent neural networks for sequence processing tasks, simulations with artificial intelligence to model complex scenarios, and edge inference techniques that allow predictions to be made on local devices. Distributed Learning also benefits from advances in Machine Learning and AutoML, which facilitate the automation of model selection and optimization processes, making learning more accessible and efficient.
