Reliable Federated TinyML Deployment for IoT Security
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, st...
| Authors | Younsoo Park, Seokhyoen Bae, Shasi Kumar Ramachandran Prabhu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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