arXiv：cs.AI· Maria-Ioana-Andrea Ivan, Orhan Ermis, Ruxandra F. Olimid·· 1 天前
Enhanced Detective：联邦学习金融欺诈检测中抵御内部投毒攻击的双阶段主动防御
Enhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning
AI 导读
针对联邦学习金融欺诈检测中恶意客户端投毒问题，研究者提出 Enhanced Detective 双阶段鲁棒聚合策略，结合 MAD、基于距离的更新过滤与 FedMedian 聚合。在不平衡信用卡数据集上针对标签翻转和权重缩放攻击进行 50 轮通信实验，40% 恶意客户端下 AUPRC 达 0.796、Recall 达 0.847。
正文
Abstract:With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged. In simple terms, FL enables multiple entities to collaboratively train AI models without sharing local data. The FL paradigm is particularly valuable in privacy-sensitive settings, such as financial services, because it enables institutions to collaboratively improve fraud-detection models while keeping sensitive transaction data local. However, FL schemes are prone to security vulnerabilities, where malicious clients can poison local data or model updates and degrade the global model, and hence the performance of the resulting model might be affected. Therefore, in this work, we propose Enhanced Detective, a dual-stage robust aggregation strategy that combines Median Absolute Deviation (MAD), distance-based update filtering, and FedMedian aggregation. Experiments on an imbalanced credit card dataset evaluate targeted label-flipping and weight-scaling attacks over 50 communication rounds. With 40% malicious clients, Enhanced Detective achieves an AUPRC of 0.796 and a Recall of 0.847.
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17523 [cs.CR] |
| (or arXiv:2610.17523v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17523 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ruxandra F. Olimid [view email]
[v1]
Tue, 13 Oct 2026 00:37:22 UTC (21,250 KB)
来源：arXiv：cs.AI · arxiv.org