Jul 2026· International Conference on Computer, Information and Telecommunication Systems· pp. 1-6· 0 citations· 10 references
Abstract
With the increasing sophistication of financial frauds, there is now a need for more advanced, secure, and scalable detection mechanisms. A fraud detection framework has been proposed that uses Federated Deep Learning (FDL) and Quantum Key Distribution (QKD) for non-IID financial data while carrying out secure communication. Using FL algorithms-FedAvg, FedAdagrad, FedAMP, and FedDyn-on partitioned client data, we demonstrate that FedDyn outperforms the other algorithms with an accuracy of 97.74%. Furthermore, we use Continuous-Variable QKD to encrypt the model updates to secure client-server communication, achieving a secure key ratio of above 98% and key rates of more than 250,000 bits/sec. Lastly, we implemented an elaborate suite of evaluations consisting of client-wise metrics, ROC curves, and t-SNE plots to validate the efficacy of our model implementation in terms of both performance and privacy preservation. Through our results, we address the brought-up importance of distributed intelligence powered by quantum encryption against advanced financial frauds.
Federated Learning trains a shared model without centralizing raw data, yet the de-facto FedAvg protocol still reveals each client's plaintext model update to the aggregation server, opening the door to gradient-inversion and membership-inference attacks. We present HE-FedSec, a federated framework that performs secure...
T. V. Van Nguyen, Vinh Thinh Le· 2026 International Workshop...· 0 citations
SecureFedShield is proposed, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments that integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture.
Kriti Mishra· International Journal of Cre...· 0 citations
Federated learning is a technology that is used to protect data privacy in machine learning. Nonetheless, in federated learning, updating the global model requires the use of gradient descent algorithm, which involves multiple rounds of interaction between entities to complete the iterative updates, inevitably incurr...
As a critical security function in the financial sector, fraud detection is commonly carried out with support from third-party risk control agencies. However, traditional detection methods risk leaking users’ private information. In this work, we implement financial fraud detection using threshold private set intersect...
Su-Fang Zhou, Liang-Yi Chen, Yi-Feng Wang et al.· IEEE Internet of Things Jour...· 0 citations
The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.
P. Banupriya, K. Vanitha· Journal of Vibration Enginee...· 0 citations
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establi...
Zhiqiang Chen, Yuchen Jiang, Ray Y. Zhong et al.· IEEE Transactions on Informa...· 0 citations
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