DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning

• URL: http://arxiv.org/abs/2603.19314v1
• Date: Sat, 14 Mar 2026 08:34:39 GMT
• Title: DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning
• Abstract summary: DPxFin is a novel framework that integrates reputation-guided adaptive differential privacy.<n>Our approach computes client reputation by evaluating the alignment between locally trained models and the global model.<n>We validate DPxFin on the Anti-Money Laundering dataset under both IID and non-IID settings using Multi Layer Perceptron (MLP)
• Score: 11.014516357629587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the modern financial system, combating money laundering is a critical challenge complicated by data privacy concerns and increasingly complex fraud transaction patterns. Although federated learning (FL) is a promising problem-solving approach as it allows institutions to train their models without sharing their data, it has the drawback of being prone to privacy leakage, specifically in tabular data forms like financial data. To address this, we propose DPxFin, a novel federated framework that integrates reputation-guided adaptive differential privacy. Our approach computes client reputation by evaluating the alignment between locally trained models and the global model. Based on this reputation, we dynamically assign differential privacy noise to client updates, enhancing privacy while maintaining overall model utility. Clients with higher reputations receive lower noise to amplify their trustworthy contributions, while low-reputation clients are allocated stronger noise to mitigate risk. We validate DPxFin on the Anti-Money Laundering (AML) dataset under both IID and non-IID settings using Multi Layer Perceptron (MLP). Experimental analysis established that our approach has a more desirable trade-off between accuracy and privacy than those of traditional FL and fixed-noise Differential Privacy (DP) baselines, where performance improvements were consistent, even though on a modest scale. Moreover, DPxFin does withstand tabular data leakage attacks, proving its effectiveness under real-world financial conditions.

Related papers

• Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
  Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
  arXiv  Detail & Related papers  (2026-03-24T17:39:09Z)
• Subgraph Federated Learning via Spectral Methods [52.40322201034717]
  FedLap is a novel framework that captures inter-node dependencies while ensuring privacy and scalability.<n>We provide a formal analysis of the privacy of FedLap, demonstrating that it preserves privacy.
  arXiv  Detail & Related papers  (2025-10-29T16:22:32Z)
• Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation [7.780051713043537]
  Federated Learning (FL) often adopts differential privacy (DP) to protect client data, but the added noise required for privacy guarantees can substantially degrade model accuracy.<n>We propose model-splitting privacy-amplified federated learning (MS-PAFL)<n>In this framework, each client's model is partitioned into a private submodel, retained locally, and a public submodel, shared for global aggregation.
  arXiv  Detail & Related papers  (2025-09-30T07:51:06Z)
• Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy [8.683908900328237]
  We propose GDPFed, which partitions clients into groups based on their privacy budgets and achieves client-level DP within each group to reduce privacy budget waste.<n>We also introduce GDPFed$+$, which integrates model sparsification to eliminate unnecessary noise and optimize per-group client sampling ratios.
  arXiv  Detail & Related papers  (2025-05-19T18:55:34Z)
• An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models [7.539653242367701]
  Federated learning (FL) enhances privacy by keeping user data on local devices.<n>Recent attacks have demonstrated that updates shared by users during training can reveal significant information about their data.<n>We propose a framework that integrates a human entity as a privacy practitioner to determine an optimal trade-off between the model's privacy and utility.
  arXiv  Detail & Related papers  (2025-02-11T23:07:14Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis [8.857443660746979]
  We introduce the DP-FedTabDiff framework, a novel integration of Differential Privacy, Federated Learning and Denoising Diffusion Probabilistic Models.<n>This framework ensures compliance with privacy regulations while maintaining data utility.
  arXiv  Detail & Related papers  (2024-12-20T17:30:58Z)
• Convergent Differential Privacy Analysis for General Federated Learning: the $f$-DP Perspective [57.35402286842029]
  Federated learning (FL) is an efficient collaborative training paradigm with a focus on local privacy. differential privacy (DP) is a classical approach to capture and ensure the reliability of private protections.
  arXiv  Detail & Related papers  (2024-08-28T08:22:21Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection [11.167661320589488]
  In real-world financial anomaly detection scenarios, the data is partitioned both vertically and horizontally. Our solution combines fully homomorphic encryption (HE), secure multi-party computation (SMPC), differential privacy (DP) Our solution won second prize in the first phase of the U.S. Privacy Enhancing Technologies (PETs) Prize Challenge.
  arXiv  Detail & Related papers  (2023-10-30T06:51:33Z)
• A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
  We propose a new differential privacy paradigm called estimate-verify-release (EVR) EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
  arXiv  Detail & Related papers  (2023-04-17T00:38:01Z)
• FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations [53.268801169075836]
  We propose FedLAP-DP, a novel privacy-preserving approach for federated learning. A formal privacy analysis demonstrates that FedLAP-DP incurs the same privacy costs as typical gradient-sharing schemes. Our approach presents a faster convergence speed compared to typical gradient-sharing methods.
  arXiv  Detail & Related papers  (2023-02-02T12:56:46Z)
• PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy [14.678119872268198]
  Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Existing FL protocol designs have been shown to be vulnerable to attacks that aim to compromise data privacy and/or model robustness. We develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography.
  arXiv  Detail & Related papers  (2021-10-22T04:08:42Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.