Federated Learning: Collaborative Machine Learning without

Centralized Training Data

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.