arXiv AI By Taym Alshoghri, Deemah H. Tashman, Mohammad Reza Gerami, Soumaya Cherkaoui

Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach

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arXiv:2606. 14515v1 Announce Type: cross Abstract: Internet of Medical Things (IoMT) devices operate under strict resource constraints while handling highly sensitive health data, making security and privacy critical concerns.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
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Reliable Federated TinyML Deployment for IoT Security

The paper explores how to combine Federated Learning with TinyML model compression techniques—such as knowledge distillation, structured pruning, and quantization—to create lightweight, privacy‑preserving intrusion detection systems for IoT devices. It evaluates these strategies within a federated training pipeline and finds that training stability is crucial; a server‑coordinated cosine learning‑rate schedule boosts Attack Recall from 46.7% to 93.85% while still allowing significant model compression and efficient edge deployment.

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Quantization in Federated Learning: Methods, Challenges and Future Directions

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arXiv AI
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A Non-Invasive Cloud-Based Migration Strategy for Post-Quantum Cybersecurity in Smart HVAC Systems: Architecture, Implementation, and Empirical Evaluation

The paper presents a non‑invasive, cloud‑based migration strategy that protects legacy smart HVAC controllers from quantum attacks without modifying the devices or vendor cloud. It introduces a Raspberry Pi 4B gateway that performs post‑quantum key encapsulation (ML‑KEM‑768) and authentication (ML‑DSA‑65) using AES‑256‑GCM, completing handshakes in 2.48 ms and sustaining 443 sessions per second. Extensive side‑channel testing shows no timing leakage or man‑in‑the‑middle vulnerabilities, and the architecture is vendor‑agnostic, becoming redundant once native PQC support is adopted.

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