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cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…