Federated Learning for Open Banking
- URL: http://arxiv.org/abs/2108.10749v1
- Date: Tue, 24 Aug 2021 14:06:16 GMT
- Title: Federated Learning for Open Banking
- Authors: Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang
- Abstract summary: In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning.
This chapter will discuss the possible challenges for applying federated learning in the context of open banking.
- Score: 42.05232310057235
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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