  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 the influence of adversaries on the
training and development of federated learning models in digital twin
6G-enabled IoT environments. We demonstrate that attackers can carry out
poisoning attacks in two different learning settings, namely: centralized
learning and federated learning, and successful attacks can severely reduce the
model's accuracy. We comprehensively evaluate the attacks on a new cyber
security dataset designed for IoT applications with three deep neural networks
under the non-independent and identically distributed (Non-IID) data and the
independent and identically distributed (IID) data. The poisoning attacks, on
an attack classification problem, can lead to a decrease in accuracy from
94.93% to 85.98% with IID data and from 94.18% to 30.04% with Non-IID.
