# Use of Federated Learning and Blockchain towards Securing Financial Services

arXiv: https://arxiv.org/abs/2303.12944 · HTML: https://mlsanity.com/paper/2303.12944/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 74 references that are in the map.
- First posted on arXiv 4 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020)
- [Towards Fair and Privacy-Preserving Federated Deep Models](https://mlsanity.com/paper/1906.01167/) (2019)

## Built on by (most cited first, 2 of 2)

- [Synergizing Foundation Models and Federated Learning: A Survey](https://mlsanity.com/paper/2406.12844/) (2024)
- [Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks](https://mlsanity.com/paper/2504.17421/) (2025)

## Abstract (arXiv)

In recent days, the proliferation of several existing and new cyber-attacks pose an axiomatic threat to the stability of financial services. It is hard to predict the nature of attacks that can trigger a serious financial crisis. The unprecedented digital transformation to financial services has been accelerated during the COVID-19 pandemic and it is still ongoing. Attackers are taking advantage of this transformation and pose a new global threat to financial stability and integrity. Many large organizations are switching from centralized finance (CeFi) to decentralized finance (DeFi) because decentralized finance has many advantages. Blockchain can bring big and far-reaching effects on the trustworthiness, safety, accessibility, cost-effectiveness, and openness of the financial sector. The present paper gives an in-depth look at how blockchain and federated learning (FL) are used in financial services. It starts with an overview of recent developments in both use cases. This paper explores and discusses existing financial service vulnerabilities, potential threats, and consequent risks. So, we explain the problems that can be fixed in financial services and how blockchain and FL could help solve them. These problems include data protection, storage optimization, and making more money in financial services. We looked at many blockchain-enabled FL methods and came up with some possible solutions that could be used in financial services to solve several challenges like cost-effectiveness, automation, and security control. Finally, we point out some future directions at the end of this study.