Machine learning, deep learning and federated learning: what are the similarities and the differences? This news came to us from Mark Tech Post in their article, “Google AI Implements Machine Learning Model That Employs Federated Learning With Differential Privacy Guarantees.”
Federated learning is also known as collaborative learning. It is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to one server, as well as to more classical decentralized approaches which often assume that local data samples are identically distributed.
Federated learning enables multiple actors to build a common, robust machine learning model without sharing data. Standard machine learning approaches require centralizing the training data on one machine or in a datacenter.
Data Harmony is a fully customizable suite of software products designed to maximize precise, efficient information management and retrieval. Our suite includes tools for taxonomy and thesaurus construction, machine aided indexing, database management, information retrieval and explainable artificial intelligence.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.