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.

翻译：人工智能生成内容（AIGC）通过采用多种生成式AI模型，已成为提升内容创作效率、质量、多样性和灵活性的前沿技术。在无线网络中部署AIGC服务有望增强用户体验。然而，现有AIGC服务提供存在若干局限性，例如预训练、微调和推理过程中的集中式训练，尤其是在无线网络中实现隐私保护的实施问题。联邦学习（FL）作为一种协同学习框架，将模型训练分布式地分配给协作数据所有者而无需数据共享，可同时提升学习效率并实现AIGC的隐私保护。为此，我们提出基于FL的技术以赋能AIGC，旨在使用户能够生成多样化、个性化且高质量的内容。此外，我们通过采用最先进的AIGC模型（即稳定扩散模型）进行FL辅助的AIGC微调案例研究。数值结果表明，我们的方案在有效降低通信成本、训练延迟和实现隐私保护方面具有优势。最后，我们指出了FL与AIGC融合的若干主要研究方向与开放性问题。