13 citations · 19 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
Network Anomaly Detection Using Federated Learning
William Marfo, Deepak K. Tosh, Shirley V. Moore
Due to the veracity and heterogeneity in network traffic, detecting anomalous events is challenging. The computational load on global servers is a significant challenge in terms of…
Adversarial Sampling for Fairness Testing in Deep Neural Network
Tosin Ige, William Marfo, Justin Tonkinson +2
In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given…
Condition monitoring and anomaly detection in cyber-physical systems
William Marfo, Deepak K. Tosh, Shirley V. Moore
The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identi…