Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework

The increasing deployment of Internet-of-Things (IoT) devices has accelerated the use of distributed learning frameworks, where data remains local while model updates are shared across decentralized systems. Although this reduces centralized data collection, it introduces privacy risks through the exchange of gradients, model parameters, and intermediate representations. A variety of privacy-preserving techniques have been proposed to address these risks, including differential privacy, cryptographic methods, and lightweight system-level approaches. However, existing surveys often evaluate these methods in isolation and lack a unified framework for comparing their effectiveness under real...

<iframe src="https://silytics.si/embed/item/85a35c93-e0ca-4e5a-ba12-a901cbf2487a" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.