Abstract
Machine Learning deployment in Embedded Systems and Edge devices offer interesting advantages compared with the Cloud-based approaches, especially from a power consumption and environmental impact perspective. However, two principal problems need to be addressed towards trustworthy Embedded ML; first, Robustness to errors: several sources of faults can jeopardize ML systems integrity; be it hardware failures, as well as malicious fault injection. Second, Security and Privacy: this includes adversarial attacks and information leakage.
Original language | English |
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Title of host publication | Embedded machine learning for cyber-physical, IoT, and edge computing: use cases and emerging challenges |
Editors | Sudeep Pasricha, Muhammad Shafique |
Place of Publication | Cham |
Publisher | Springer Nature Switzerland |
Pages | 497-517 |
Number of pages | 21 |
ISBN (Electronic) | 9783031406775 |
ISBN (Print) | 9783031406768 |
DOIs | |
Publication status | Published - 07 Oct 2023 |