Description: Federated learning is a machine learning approach that allows algorithms to be trained on multiple decentralized devices that have local data samples. This method is based on the premise that data can remain in its original locations, helping to preserve the privacy and security of sensitive information. Instead of centralizing data on a server, federated learning enables models to be trained locally on each device, with only the updated model parameters sent to a central server. This not only reduces the need to transfer large volumes of data but also improves the efficiency of the training process by leveraging distributed computing resources. Furthermore, federated learning is particularly relevant in contexts where privacy is crucial, such as in various industries like healthcare, finance, and mobile applications. This approach also allows models to adapt to the peculiarities of local data, which can result in improved performance compared to models trained on centralized datasets. In summary, federated learning represents a significant evolution in the field of machine learning, offering a balance between model effectiveness and data privacy protection.
History: The concept of federated learning was first introduced by researchers at Google in 2016, aiming to enable the training of machine learning models on mobile devices without compromising user privacy. Since then, it has evolved and been adopted in various applications, especially in the field of artificial intelligence and the processing of sensitive data.
Uses: Federated learning is primarily used in applications where data privacy is paramount, such as in the healthcare sector, where patient data cannot be shared. It is also applied in the development of language models on mobile devices, allowing models to adapt to user preferences without sending personal data to central servers.
Examples: An example of federated learning is Google’s text prediction system, which improves its performance by learning from users’ writing patterns without storing their data. Another case is the use in health devices that collect patient data and train models to detect diseases without compromising patient privacy.
