发表机构
The Pennsylvania State University; Elizabethtown College（宾夕法尼亚州立大学; 伊丽莎白敦学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将联邦学习与TinyML模型压缩结合用于物联网入侵检测，通过服务器协调的余弦学习率调度将攻击召回率从46.7%提升至93.85%，实现轻量级隐私保护部署。
AI 中文摘要
物联网（IoT）设备的日益部署增加了对隐私保护入侵检测系统的需求，这些系统直接在资源受限的硬件上运行。联邦学习使得在不共享原始数据的情况下进行协作模型训练成为可能，但传统的联邦模型通常过于庞大且不稳定，难以在微控制器级设备上部署。微型机器学习（TinyML）技术能够实现紧凑的神经网络，但通常设计用于仅推理的工作负载。本研究探讨了将联邦学习与基于TinyML的模型压缩相结合，用于物联网环境中的入侵检测。我们在联邦训练流程中评估了包括知识蒸馏、结构化剪枝和量化在内的压缩策略。初步结果表明，训练稳定性在联邦微型机器学习系统中起着关键作用。特别是，服务器协调的余弦学习率调度将攻击召回率从46.7%提升至93.85%，同时实现了显著的模型压缩和高效的边缘部署。这些发现为设计轻量级且隐私保护的物联网设备入侵检测系统提供了见解。
英文摘要
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.
