Federated Learning for Open Banking
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- Federated Learning: Privacy and Incentive, 2020, 12500 LNCS, pp. 240-254
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|Long2020_Chapter_FederatedLearningForOpenBankin.pdf||Published version||448.73 kB|
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© 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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