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.