  Distributed Artificial Intelligence (AI) model training over mobile edge
networks encounters significant challenges due to the data and resource
heterogeneity of edge devices. The former hampers the convergence rate of the
global model, while the latter diminishes the devices' resource utilization
efficiency. In this paper, we propose a generative AI-empowered federated
learning to address these challenges by leveraging the idea of FIlling the
MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a
resource-aware data augmentation method that effectively mitigates the data
heterogeneity while ensuring efficient FL training. We first quantify the
relationship between the training data amount and the learning performance. We
then study the FIMI optimization problem with the objective of minimizing the
device-side overall energy consumption subject to required learning performance
constraints. The decomposition-based analysis and the cross-entropy searching
method are leveraged to derive the solution, where each device is assigned
suitable AI-synthesized data and resource utilization policy. Experiment
results demonstrate that FIMI can save up to 50% of the device-side energy to
achieve the target global test accuracy in comparison with the existing
methods. Meanwhile, FIMI can significantly enhance the converged global
accuracy under the non-independently-and-identically distribution (non-IID)
data.
