Input-Layer Starvation: Why Per-Layer Pruning Breaks IoT Intrusion Detectors
cs.CR, cs.LG
Submitted: 2026-09-25
Updated: 2026-09-25
Code: https://github.com/anasbiswas1/iot-trust-compression
Terminology
Sources
- To prune, or not to prune: exploring the efficacy of pruning for model compression
- The State of Sparsity in Deep Neural Networks
- What Do Compressed Deep Neural Networks Forget?
- Characterising Bias in Compressed Models
- Prune Responsibly
- Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning
- Signal Collapse in One-Shot Pruning: When Sparse Models Fail to Distinguish Neural Representations
- Rethinking "Batch" in BatchNorm
- Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge
- Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
- Reliable Federated TinyML Deployment for IoT Security
- Streamlining Tensor and Network Pruning in PyTorch
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