Explainable Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring

FJ Piran, PP Poduval, HE Barkam, M Imani… - arxiv preprint arxiv …, 2024 - arxiv.org
In-situ sensing, in conjunction with learning models, presents a unique opportunity to
address persistent defect issues in Additive Manufacturing (AM) processes. However, this …

Distributed cryptosystem for service-oriented smart manufacturing

A Krall, D Finke, H Yang - IISE Transactions, 2024 - Taylor & Francis
Advanced sensing and cloud systems propel the rapid advancements of service-oriented
smart manufacturing. As a result, there is widespread generation and proliferation of data in …

Federated Learning on Distributed and Encrypted Data for Smart Manufacturing

T Kuo, H Yang - Journal of Computing and …, 2024 - asmedigitalcollection.asme.org
Industry 4.0 drives exponential growth in the amount of operational data collected in
factories. These data are commonly distributed and stored in different business units or …

Data privacy preserving for centralized robotic fault diagnosis with modified dataset distillation

T Wang, Y Huang, Y Liu… - … of Computing and …, 2024 - asmedigitalcollection.asme.org
Industrial robots generate monitoring data rich in sensitive information, often making
enterprises reluctant to share, which impedes the use of data in fault diagnosis modeling …

Trustworthy IoT Infrastructures: Privacy-Preserving Federated Learning with Efficient Secure Aggregation for Cybersecurity

D Kumar, PP Pawar, MK Meesala… - 2024 International …, 2024 - ieeexplore.ieee.org
Smart gadgets have become ubiquitous due to the fast development of the Internet of Things
(IoT). In an effort to recover the precision and excellence of their services, service providers …