4 citations · 5 across the 7 of their papers we have counts for
5 papers · 1 filter
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
William Marfo, Deepak Tosh, Shirley Moore +2
Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where diverse client configurations and network conditions im…
Network Anomaly Detection in Distributed Edge Computing Infrastructure
William Marfo, Enrique A. Rico, Deepak K. Tosh +1
As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Tr…
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems
William Marfo, Deepak K. Tosh, Shirley V. Moore
Detecting and localizing anomalies in cyber-physical systems (CPS) has become increasingly challenging as systems grow in complexity, particularly due to varying sensor reliability…
Efficient Client Selection in Federated Learning
William Marfo, Deepak K. Tosh, Shirley V. Moore
Federated Learning (FL) enables decentralized machine learning while preserving data privacy. This paper proposes a novel client selection framework that integrates differential pr…
Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case
William Marfo, Deepak K. Tosh, Shirley V. Moore
Federated Learning (FL) has become a widely used approach for training machine learning models on decentralized data, addressing the significant privacy concerns associated with tr…