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Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where diverse client configurations and network conditions impact efficiency and detection accuracy.
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection
These issues include the use of overly complex and opaque ML models, unaccounted data imbalances and correlated features, inconsistent influential features across different explanation methods, the inconsistencies stemming from the constituents of a learning process, and the implausible utility of explanations.
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning
With the advance of the powerful heterogeneous, parallel and distributed computing systems and ever increasing immense amount of data, machine learning has become an indispensable part of cutting-edge technology, scientific research and consumer products.
