EPC-TinyAD: An Energy- and Privacy-Aware Compressed TinyML Framework for Reliable Industrial Anomaly Detection on Resource-Constrained Edge Devices

Real-time industrial anomaly detection is increasingly shifting from cloud-based diagnosis to edge intelligence deployed close to machines. However, practical industrial scenarios are constrained by scarce fault samples, unknown anomaly types, cross-machine distribution shifts, strict false alarm requirements, data privacy restrictions, and limited edge device resources. To address these challenges, this paper proposes EPC-TinyAD, an energy- and privacy-aware compressed TinyML framework for reliable industrial anomaly detection on resource-constrained edge devices. EPC-TinyAD follows a normal-only learning paradigm and employs a tiny depthwise-separable CNN autoencoder as the deployable student model, guided by a wider teacher autoencoder during training. Instead of relying solely on reconstruction error, the proposed anomaly score integrates spectrogram reconstruction deviation, compact normal-center distance, and teacher–student distillation discrepancy. Masked spectrogram modeling is introduced to enhance few-shot normal representation learning, while domain-adversarial invariant embedding improves cross-machine generalization. To support reliable deployment, split and adaptive conformal thresholding calibrate anomaly decisions under target false alarm rates. Furthermore, federated training with clipped and noisy updates reduces raw industrial data exposure, and energy-aware compression integrates pruning, INT8 size estimation, model export, latency benchmarking, and Pareto analysis. Experiments on industrial anomaly detection data demonstrate that EPC-TinyAD achieves 96.5% accuracy, 95.4% recall, 96.1% F1 score, 0.964 AUROC, and 0.952 AUPRC over five random seeds. These results indicate that EPC-TinyAD provides a reliable, lightweight, privacy-aware, and deployment-oriented framework for industrial edge anomaly detection, while future work will further validate its runtime memory, latency, and power consumption on physical Raspberry Pi-, Jetson-, or MCU-class edge devices.