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Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study. (arXiv:2303.11745v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
Federated edge learning can be essential in supporting privacy-preserving,
artificial intelligence (AI)-enabled activities in digital twin 6G-enabled
Internet of Things (IoT) environments. However, we need to also consider the
potential of attacks targeting the underlying AI systems (e.g., adversaries
seek to corrupt data on the IoT devices during local updates or corrupt the
model updates); hence, in this article, we propose an anticipatory study for
poisoning attacks in federated edge learning for digital twin 6G-enabled IoT
environments. Specifically, we study …
adversaries article artificial artificial intelligence attacks corrupt data devices digital digital twin edge environments intelligence internet internet of things iot iot devices local poisoning privacy study systems targeting things updates