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.
By Younsoo Park, Seokhyoen Bae, Shasi Kumar Ramachandran Prabhu, Suman Saha, Peilong Li
arXiv:2606. 26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data.
By Farwa Ikram, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino
arXiv:2412.06210v3 Announce Type: replace
Abstract: With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use...
By Jiechao Gao, Yuangang Li, Jie Wang, Yue Zhao, Michael Lepech, Brad Campbell
arXiv:2607. 21647v1 Announce Type: new Abstract: Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services.
By Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
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.
By Mahedee Zaman Moon, Kaysarul Anas Apurba, Md Hasibul Hasan, Sk Md Mizanur Rahman
arXiv:2609.24669v1 Announce Type: cross
Abstract: Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things...
By Esin Ece Ayd{\i}n, \c{S}erif Bahtiyar, G\"urkan G\"ur
Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things (IoT). To support time-sensitive and resource-cons...
arXiv:2607. 10467v1 Announce Type: cross Abstract: Healthcare organizations often cannot freely centralize patient data because medical records are sensitive, regulated, and institutionally controlled.
By Sakshi Gorkhali, Jonesh Shrestha
arXiv:2608. 13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training.
By Chun-Hua Lin, Samuel Yen-Chi Chen, Yu-Chao Hsu, Kuo-Chung Peng, Jiun-Cheng Jiang, Chi-Sheng Chen, Tai-Yue Li, Nan-Yow Chen, En-Jui Kuo, Hsi-Sheng Goan
arXiv:2606. 06895v1 Announce Type: cross Abstract: The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, establishing urgent demand for trustworthy data provenance, cross-organizational governance, and incentive-compatible sharing across decentralized ecosystems.
By Song Guo, Huawei Huang, Dongping Liu, Aoyu Zhang, Luyao Zhang
arXiv:2608.20580v1 Announce Type: cross
Abstract: Federated learning (FL) is vulnerable to multi-level attacks. However, existing methods address them separately, leaving FL exposed to data leakage,...
By Xinyun Liu, Zhi Lu, Yu Chen, Ronghua Xu
arXiv:2609.25082v1 Announce Type: new
Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fr...
By Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria