Federated Financial Forecasting: Privacy-Preserving AI Architectures for  Multi-Entity Budget Consolidation

Ramsundernag Changalva

Federated Financial Forecasting: Privacy-Preserving AI Architectures for Multi-Entity Budget Consolidation

Authors

Ramsundernag Changalva

Keywords:

Federated Learning, Differential Privacy, Multi-Party Computation, Financial Forecasting, Budget Consolidation, Homomorphic Encryption, Asynchronous Architectures, Fraud Detection, Coopetition, Regulatory Technology.

Abstract

The joint attempt of budgets and the projection of future paths of independent activities pose aserious contradiction between usefulness of algorithms and secrecy of data. For training strong predictive models that forecast distress

References

Abdel-Basset, M., Hawash, H., & Moustafa, N. (2022). Toward privacy preserving federated learning in Internet of Vehicular Things: Challenges and future directions. IEEE Consumer Electronics Magazine, 11(6), 56 66. https://doi.org/10.1109/mce.2021.3117232

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Published

2023-09-16

How to Cite

Ramsundernag Changalva. (2023). Federated Financial Forecasting: Privacy-Preserving AI Architectures for Multi-Entity Budget Consolidation. Journal of Computational Analysis and Applications (JoCAAA), 31(3), 946–964. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5096