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
Автор: Emily Glanz, Nova Fallen
Издательство: O’Reilly Media, Inc.
Год: 2021-10-14
Язык: английский
Формат: epub
Размер: 10.2 MB
The use of Artificial Intelligence (AI) and Machine Learning (ML) technologies is growing rapidly. AI and ML are empowering many application domains, such as natural language processing (NLP), image classification, and recommendation systems. You’ve probably seen this yourself: smartphone keyboard autocorrect features, face unlock capabilities, and movie recommendation algorithms are all powered by Machine Learning. One of the key reasons for the success of ML technologies today is the accessibility and availability of massive amounts of data. The key idea behind Federated Learning (FL) is that it is possible to bring model training to the location where the data was generated and lives, removing the requirement for centralized data collection. Federated Learning is a type of Machine Learning in which a set of clients collaboratively train a model on local data under the orchestration of a central server, without sharing raw data with each other or the server.