2 citations · 3 across the 7 of their papers we have counts for
7 papers
DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning
Renuga Kanagavelu, Manjil Nepal, Ning Peiyan +6
In the modern financial system, combating money laundering is a critical challenge complicated by data privacy concerns and increasingly complex fraud transaction patterns. Althoug…
Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillation
Yiming Qian, Thorsten Neumann, Xueyining Huang +4
We propose an explainable, privacy-preserving dataset distillation framework for collaborative financial fraud detection. A trained random forest is converted into transparent, axi…
Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning
Danni Peng, Yuan Wang, Kangning Cai +6
In healthcare, federated learning (FL) is a widely adopted framework that enables privacy-preserving collaboration among medical institutions. With large foundation models (FMs) de…
AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation
Bo Gao, Yuan Wang, Qingsong Wei +3
Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure transactions, yet these oracles are highly vulnerable to manipulation, enabling attacker…
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning
Danni Peng, Yuan Wang, Huazhu Fu +4
Personalized federated learning (PFL) studies effective model personalization to address the data heterogeneity issue among clients in traditional federated learning (FL). Existing…
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
Yuan Wang, Huazhu Fu, Renuga Kanagavelu +3
The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framew…
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views
Renuga Kanagavelu, Kinshuk Dua, Pratik Garai +6
Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be n…