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Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications

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  • David Byrd
  • Antigoni Polychroniadou

Abstract

Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within its own local systems. This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters. Most federated learning systems therefore use differential privacy to introduce noise to the parameters. This adds uncertainty to any attempt to reveal private client data, but also reduces the accuracy of the shared model, limiting the useful scale of privacy-preserving noise. A system can further reduce the coordinating server's ability to recover private client information, without additional accuracy loss, by also including secure multiparty computation. An approach combining both techniques is especially relevant to financial firms as it allows new possibilities for collaborative learning without exposing sensitive client data. This could produce more accurate models for important tasks like optimal trade execution, credit origination, or fraud detection. The key contributions of this paper are: We present a privacy-preserving federated learning protocol to a non-specialist audience, demonstrate it using logistic regression on a real-world credit card fraud data set, and evaluate it using an open-source simulation platform which we have adapted for the development of federated learning systems.

Suggested Citation

  • David Byrd & Antigoni Polychroniadou, 2020. "Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications," Papers 2010.05867, arXiv.org.
  • Handle: RePEc:arx:papers:2010.05867
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    Cited by:

    1. Vivek Kumar Prasad & Pronaya Bhattacharya & Darshil Maru & Sudeep Tanwar & Ashwin Verma & Arunendra Singh & Amod Kumar Tiwari & Ravi Sharma & Ahmed Alkhayyat & Florin-Emilian Čšurcanu & Maria Simona Ra, 2022. "Federated Learning for the Internet-of-Medical-Things: A Survey," Mathematics, MDPI, vol. 11(1), pages 1-47, December.
    2. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    3. Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
    4. Xiao-Yang Liu & Ziyi Xia & Jingyang Rui & Jiechao Gao & Hongyang Yang & Ming Zhu & Christina Dan Wang & Zhaoran Wang & Jian Guo, 2022. "FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning," Papers 2211.03107, arXiv.org.
    5. David Cerezo S'anchez, 2021. "JUBILEE: Secure Debt Relief and Forgiveness," Papers 2109.07267, arXiv.org.

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