Federated learning for privacy-preserving data access

Małgorzata Śmietanka, Hirsh Pithadia, Philip Treleaven
2021 International Journal of Data Science and Big Data Analytics  
Federated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets. Companies collect huge amounts of historic and real-time data to drive their business and collaborate with other organizations. However, data privacy is becoming increasingly important because of regulations (e.g., EU GDPR) and the need to protect their sensitive and personal data. Companies need to manage data access: firstly within their organizations
more » ... o they can control staff access), and secondly protecting raw data when collaborating with third parties. What is more, companies are increasingly looking to 'monetize' the data they've collected. However, under new legislations, utilizing data by different organization is becoming increasingly difficult (Yu, 2016) . Federated learning pioneered by Google is the emerging privacy-preserving data technology and also a new class of distributed machine learning models. This paper discusses federated learning as a solution for privacy-preserving data access and distributed machine learning applied to distributed data sets. It also presents a privacy-preserving federated learning infrastructure.
doi:10.51483/ijdsbda.1.2.2021.1-13 fatcat:b4rbxexaerburf5xrscizs27ae