Federated Learning: Collaborative Machine Learning without Centralized Training Data
Federated learning allows several actors to collaborate on the development of a single, robust machine learning model without sharing data, allowing crucial issues such as data privacy, data security, data access rights, and access to heterogeneous data to be addressed.
Abstract
Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm without transferring data samples across numerous decentralized edge devices or servers. This strategy differs from standard centralized machine learning techniques in which all local datasets are uploaded to a single server, as well as more traditional decentralized alternatives, which frequently presume that local data samples are uniformly distributed. Federated learning allows several actors to collaborate on the development of a single, robust machine learning model without sharing data, allowing crucial issues such as data privacy, data security, data access rights, and access to heterogeneous data to be addressed. Defence, telecommunications, internet of things, and pharmaceutical industries are just a few of the sectors where it has applications.