2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering
Daniel Commey, Matilda Nkoom, Yousef Alsenani +2
Virtual Asset Service Providers (VASPs) face a fundamental tension between regulatory compliance and user privacy when detecting cross-institutional money laundering. Current appro…
FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning
Daniel Commey, Kamel Abbad, Garth V. Crosby +1
Communication overhead remains a primary bottleneck in federated learning (FL), particularly for applications involving mobile and IoT devices with constrained bandwidth. This work…
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs
Daniel Commey, Benjamin Appiah, Griffith S. Klogo +1
Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information thr…
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
Daniel Commey, Rebecca A. Sarpong, Griffith S. Klogo +2
Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-p…