Description: The Learning Framework in the context of Federated Learning refers to a structured approach that provides guidelines and principles for implementing machine learning algorithms collaboratively and in a decentralized manner. This framework allows multiple entities, such as devices or institutions, to collaborate in training artificial intelligence models without the need to share sensitive or private data. Instead of centralizing information, federated learning enables data to remain in its original location, enhancing privacy and security. This approach is particularly relevant in a world where data protection is crucial, as it allows organizations to benefit from machine learning without compromising the confidentiality of information. Key features of the framework include the ability to handle heterogeneous data, optimizing models through the aggregation of local updates, and reducing latency by avoiding the transfer of large volumes of data. In summary, the Learning Framework in Federated Learning is an innovative solution that promotes collaboration and efficiency in the development of machine learning models while respecting data privacy.
History: The concept of Federated Learning was first introduced in 2016 by researchers at Google, who published a paper titled ‘Communication-Efficient Learning of Deep Networks from Decentralized Data’. Since then, it has evolved and become an active area of research, with multiple academic contributions and practical applications across various industries.
Uses: Federated Learning is used in various applications, such as improving prediction models on mobile devices, where user data is not shared directly. It is also applied in the healthcare sector to train diagnostic models without compromising patient privacy, as well as in the financial industry to detect fraud without revealing sensitive information.
Examples: A practical example of Federated Learning is Google’s text prediction system, which improves its predictive typing model using data from users’ devices without accessing personal information. Another case is its use in hospitals to train AI models that assist in diagnosing diseases based on clinical data distributed among different institutions.
