Reliable Federated TinyML Deployment for IoT Security

2026-09-22

AI Summary

研究结合联邦学习与TinyML模型压缩技术，通过知识蒸馏、结构化剪枝和量化方法解决IoT设备上的隐私保护入侵检测问题。

Abstract

The growing deployment of Internet of Things (IoT) devices has increased the need for privacy-preserving intrusion detection systems that operate directly on resource-constrained hardware. Federated Learning enables collaborative model training without sharing raw data, but conventional federated models are often too large and unstable for deployment on microcontroller-class devices. TinyML techniques enable compact neural networks but are typically designed for inference-only workloads. This work investigates combining Federated Learning with TinyML-based model compression for intrusion detection in IoT environments. We evaluate compression strategies including knowledge distillation, structured pruning, and quantization within a federated training pipeline. Preliminary results show that training stability plays a critical role in federated TinyML systems. In particular, server-coordinated cosine learning-rate scheduling improves Attack Recall from 46.7% to 93.85% while enabling substantial model compression and efficient edge deployment. These findings provide insights for designing lightweight and privacy preserving intrusion detection systems for IoT devices.

Problem

• Federated Learning
• TinyML
• Intrusion Detection
• IoT Security
• Model Compression

Innovation

• Federated Learning
• TinyML
• model compression
• intrusion detection
• cosine learning-rate scheduling