2 citations · 3 across the 5 of their papers we have counts for
5 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…
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…
History-Aware and Dynamic Client Contribution in Federated Learning
Bishwamittra Ghosh, Debabrota Basu, Fu Huazhu +6
Federated Learning (FL) is a collaborative machine learning (ML) approach, where multiple clients participate in training an ML model without exposing their private data. Fair and…
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…
Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning
Renuga Kanagavelu, Zengxiang Li, Juniarto Samsudin +7
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where…