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2020 | OriginalPaper | Buchkapitel

Federated Learning for Open Banking

verfasst von : Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang

Erschienen in: Federated Learning

Verlag: Springer International Publishing

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Abstract

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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Titel: Federated Learning for Open Banking
verfasst von: Guodong Long
Yue Tan
Jing Jiang
Chengqi Zhang
Verlag: Springer International Publishing
Buch: Federated Learning
Print ISBN: 978-3-030-63075-1

Electronic ISBN: 978-3-030-63076-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-63076-8_17

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