Reliable Federated TinyML Deployment for IoT Security

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.

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2609.27202 [cs.CR]

(or arXiv:2609.27202v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.27202 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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