Federated Learning for Open Banking

By Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang. Federated Learning, 2021

370 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/ec0a52bc9923275f7436b38fbc8c79f10cb83cf2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Guodong Long
2. Jing Jiang
3. Chengqi Zhang
4. Tianyi Zhou
5. Yue Tan
6. Bo Li
7. Lu Liu
8. Tao Shen

In Information Systems

1. Jing Jiang
2. Guodong Long
3. Tianyi Zhou
4. Yue Tan
5. Lu Liu
6. Bo Li
7. Bowen Zhou
8. Jinfeng Yi

Citations in the corpus

• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach (2022, methodology): “So far, FL and PFL have been widely used in computer vision [8], natural language processing [9], graph data mining [10, 11], and some practical applications, e.g., healthcare [12], finance [13], mobile Internet [14, 15], etc.”
• Trustworthy AI: From Principles to Practices (2021, methodology): “It has been applied to numerous domains, such as healthcare [282, 299] and finance [223].”
• Multi-center federated learning: clients clustering for better personalization (2020, methodology): “It has been applied to various industry applications, such as banking [37], healthcare [36, 48, 57], and mobile internet applications [21].”
• Vertical Federated Learning: Concepts, Advances, and Challenges (2022, neutral): “[179]discussedtheapplications andopenchallengesforFLinopenbanking.”
• FedProto: Federated Prototype Learning across Heterogeneous Clients (2021, background): “However, in real-world applications, model heterogeneity is common because of varying hardware and computation capabilities across clients (Long et al. 2020).”