  Federated Edge Learning (FEL) allows edge nodes to train a global deep
learning model collaboratively for edge computing in the Industrial Internet of
Things (IIoT), which significantly promotes the development of Industrial 4.0.
However, FEL faces two critical challenges: communication overhead and data
privacy. FEL suffers from expensive communication overhead when training
large-scale multi-node models. Furthermore, due to the vulnerability of FEL to
gradient leakage and label-flipping attacks, the training process of the global
model is easily compromised by adversaries. To address these challenges, we
propose a communication-efficient and privacy-enhanced asynchronous FEL
framework for edge computing in IIoT. First, we introduce an asynchronous model
update scheme to reduce the computation time that edge nodes wait for global
model aggregation. Second, we propose an asynchronous local differential
privacy mechanism, which improves communication efficiency and mitigates
gradient leakage attacks by adding well-designed noise to the gradients of edge
nodes. Third, we design a cloud-side malicious node detection mechanism to
detect malicious nodes by testing the local model quality. Such a mechanism can
avoid malicious nodes participating in training to mitigate label-flipping
attacks. Extensive experimental studies on two real-world datasets demonstrate
that the proposed framework can not only improve communication efficiency but
also mitigate malicious attacks while its accuracy is comparable to traditional
FEL frameworks.
