  Artificial intelligence generated content (AIGC) has emerged as a promising
technology to improve the efficiency, quality, diversity and flexibility of the
content creation process by adopting a variety of generative AI models.
Deploying AIGC services in wireless networks has been expected to enhance the
user experience. However, the existing AIGC service provision suffers from
several limitations, e.g., the centralized training in the pre-training,
fine-tuning and inference processes, especially their implementations in
wireless networks with privacy preservation. Federated learning (FL), as a
collaborative learning framework where the model training is distributed to
cooperative data owners without the need for data sharing, can be leveraged to
simultaneously improve learning efficiency and achieve privacy protection for
AIGC. To this end, we present FL-based techniques for empowering AIGC, and aim
to enable users to generate diverse, personalized, and high-quality content.
Furthermore, we conduct a case study of FL-aided AIGC fine-tuning by using the
state-of-the-art AIGC model, i.e., stable diffusion model. Numerical results
show that our scheme achieves advantages in effectively reducing the
communication cost and training latency and privacy protection. Finally, we
highlight several major research directions and open issues for the convergence
of FL and AIGC.
