Federated Learning for Privacy-Preserving Data Access

16 Pages Posted: 10 Nov 2020

See all articles by Małgorzata Śmietanka

Małgorzata Śmietanka

University College London - Department of Computer Science

Hirsh Pithadia

University College London - Department of Computer Science

Philip Treleaven

University College London

Date Written: September 15, 2020

Abstract

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 organisations. 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 (so 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, utilising 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.

Keywords: Federated learning, Machine Learning, Data Technology, InsurTech, RegTech, AI, ML

JEL Classification: C63

Suggested Citation

Śmietanka, Małgorzata and Pithadia, Hirsh and Treleaven, Philip, Federated Learning for Privacy-Preserving Data Access (September 15, 2020). Available at SSRN: https://ssrn.com/abstract=3696609 or http://dx.doi.org/10.2139/ssrn.3696609

Małgorzata Śmietanka (Contact Author)

University College London - Department of Computer Science ( email )

United Kingdom

Hirsh Pithadia

University College London - Department of Computer Science ( email )

United Kingdom

HOME PAGE: http://www0.cs.ucl.ac.uk/people/H.Pithadia.html

Philip Treleaven

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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