mlsanityData to 13 Oct 2026

Federated Learning for Open Banking

arXiv 2108.10749 ↗First posted 24 Aug 2021cs.DC cs.LGcited by 141

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 141 papers in the map (data through 13 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

23 of 43 references.

References · 23

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,180
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,981
  3. 2016Deep Learning with Differential Privacycited by 2,718
  4. 2018Federated Learning with Non-IID Datacited by 1,106
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 616
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2019Federated Learning with Personalization Layerscited by 539
  8. 2020An Efficient Framework for Clustered Federated Learningcited by 508
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 502
  10. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  11. 2019Agnostic Federated Learningcited by 453
  12. 2019Federated Machine Learning: Concept and Applicationscited by 411
11 more
  1. 2020Adaptive Personalized Federated Learningcited by 355
  2. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 324
  3. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 323
  4. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  5. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  6. 2020Federated Learning of a Mixture of Global and Local Modelscited by 209
  7. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  8. 2019One-Shot Federated Learningcited by 125
  9. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  10. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  11. 2020Privacy in Deep Learning: A Surveycited by 71

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 4; 2022: 26; 2023: 34; 2024: 37; 2025: 30; 2026: 10.

141 papers.

Sort:

By area:

  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  3. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  4. 2022When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methodscited by 75
  5. 2022Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakagecited by 72
  6. 2023Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Datacited by 40
  7. 2024Advances and Open Challenges in Federated Foundation Modelscited by 34
  8. 2022Personalized Federated Learning With Graphcited by 29
  9. 2023Federated Generative Learning with Foundation Modelscited by 23
  10. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  11. 2024Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learningcited by 23
  12. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
38 more
  1. 2022Proportional Fairness in Federated Learningcited by 20
  2. 2023Towards Quantum Federated Learningcited by 20
  3. 2023Dual Personalization on Federated Recommendationcited by 19
  4. 2021Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learningcited by 17
  5. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  6. 2022TabLeak: Tabular Data Leakage in Federated Learningcited by 13
  7. 2022PGFed: Personalize Each Client's Global Objective for Federated Learningcited by 13
  8. 2023Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learningcited by 13
  9. 2025Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspectioncited by 13
  10. 2022FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networkscited by 12
  11. 2022Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applicationscited by 12
  12. 2022FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labelscited by 12
  13. 2022Federated Learning Attacks and Defenses: A Surveycited by 12
  14. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  15. 2023FLGo: A Fully Customizable Federated Learning Platformcited by 12
  16. 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
  17. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
  18. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  19. 2024Adaptive Gradient Clipping for Robust Federated Learningcited by 10
  20. 2022Deep Leakage from Model in Federated Learningcited by 9
  21. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  22. 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
  23. 2025Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attackscited by 9
  24. 2023Improving Accelerated Federated Learning with Compression and Importance Samplingcited by 8
  25. 2023PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learningcited by 8
  26. 2024Promoting Data and Model Privacy in Federated Learning through Quantized LoRAcited by 8
  27. 2024Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneitycited by 8
  28. 2022FedSyn: Synthetic Data Generation using Federated Learningcited by 7
  29. 2023Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharingcited by 7
  30. 2023Breaching FedMD: Image Recovery via Paired-Logits Inversion Attackcited by 7
  31. 2024Robust Training of Federated Models with Extremely Label Deficiencycited by 7
  32. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  33. 2022An Efficient and Robust System for Vertically Federated Random Forestcited by 6
  34. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  35. 2023Understanding Deep Gradient Leakage via Inversion Influence Functionscited by 6
  36. 2023Fake It Till Make It: Federated Learning with Consensus-Oriented Generationcited by 6
  37. 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scalercited by 6
  38. 2024Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learningcited by 6

50 most cited of 141 All 141 (JSON)