Federated Anomaly Detection: Scaling Edge Security with Spark & Kubernetes
Failed to add items
Add to Cart failed.
Add to Wish List failed.
Remove from wishlist failed.
Adding to library failed
Follow podcast failed
Unfollow podcast failed
Federated Anomaly Detection: Scaling Edge Security with Spark & Kubernetes
-
Narrated by:
-
By:
About this listen
Tackling network intrusions on distributed edge systems without compromising user privacy is a major engineering challenge. This episode unpacks a research paper proposing a novel solution using Federated Learning integrated with Apache Spark and Kubernetes. Explore how this architecture allows collaborative model training for anomaly detection directly on edge devices, keeping raw data local and secure. We discuss its impressive accuracy on both general network traffic and specialized automotive attack datasets. Discover the clever use of adaptive checkpointing based on the Weibull distribution to enhance fault tolerance in real-world conditions. Understand the practical benefits of this scalable, robust framework for securing modern edge computing infrastructure.
Read the original paper: http://arxiv.org/abs/2503.05700v1
Music: 'The Insider - A Difficult Subject'