  Federated learning (FL) has emerged as a promising paradigm that enables
clients to collaboratively train a shared global model without uploading their
local data. To alleviate the heterogeneous data quality among clients,
artificial intelligence-generated content (AIGC) can be leveraged as a novel
data synthesis technique for FL model performance enhancement. Due to various
costs incurred by AIGC-empowered FL (e.g., costs of local model computation and
data synthesis), however, clients are usually reluctant to participate in FL
without adequate economic incentives, which leads to an unexplored critical
issue for enabling AIGC-empowered FL. To fill this gap, we first devise a data
quality assessment method for data samples generated by AIGC and rigorously
analyze the convergence performance of FL model trained using a blend of
authentic and AI-generated data samples. We then propose a data quality-aware
incentive mechanism to encourage clients' participation. In light of
information asymmetry incurred by clients' private multi-dimensional
attributes, we investigate clients' behavior patterns and derive the server's
optimal incentive strategies to minimize server's cost in terms of both model
accuracy loss and incentive payments for both complete and incomplete
information scenarios. Numerical results demonstrate that our proposed
mechanism exhibits highest training accuracy and reduces up to 53.34% of the
server's cost with real-world datasets, compared with existing benchmark
mechanisms.
