Federated Learning for Open Banking

Long, G; Tan, Y; Jiang, J; Zhang, C

Federated Learning for Open Banking

Long, G

Tan, Y Jiang, J

Zhang, C

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Federated Learning: Privacy and Incentive, 2020, 12500 LNCS, pp. 240-254
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515
dc.contributor.author	Tan, Y
dc.contributor.author	Jiang, J https://orcid.org/0000-0001-5301-7779
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154
dc.date.accessioned	2021-01-06T06:57:44Z
dc.date.available	2021-01-06T06:57:44Z
dc.date.issued	2020-01-01
dc.identifier.citation	Federated Learning: Privacy and Incentive, 2020, 12500 LNCS, pp. 240-254
dc.identifier.isbn	978-3-030-63075-1
dc.identifier.uri	http://hdl.handle.net/10453/145120
dc.description.abstract	© 2020, Springer Nature Switzerland AG. Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services. In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning. This is a just-in-time technology that can learn intelligent models in a decentralized training manner. The most attractive aspect of federated learning is its ability to decompose model training into a centralized server and distributed nodes without collecting private data. This kind of decomposed learning framework has great potential to protect users’ privacy and sensitive data. Therefore, federated learning combines naturally with an open banking data marketplaces. This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.
dc.format.extent	18
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Federated Learning: Privacy and Incentive
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatic
dc.relation.isbasedon	10.1007/978-3-030-63076-8_17
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Federated Learning for Open Banking
dc.type	Chapter
utslib.citation.volume	12500 LNCS
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-01-06T06:57:41Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	12500 LNCS
dc.location	Switzerland

Abstract:

© 2020, Springer Nature Switzerland AG. Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services. In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning. This is a just-in-time technology that can learn intelligent models in a decentralized training manner. The most attractive aspect of federated learning is its ability to decompose model training into a centralized server and distributed nodes without collecting private data. This kind of decomposed learning framework has great potential to protect users’ privacy and sensitive data. Therefore, federated learning combines naturally with an open banking data marketplaces. This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.

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http://hdl.handle.net/10453/145120