Recently, Artificial Intelligence Generated Content (AIGC) has gained significant attention from society, especially with the rise of Generative AI (GAI) techniques such as ChatGPT, GPT-4 [ 165 ], DALL-E-3 [ 184 ], and Sora [ 137 ]. AIGC involves using AI models to create digital content, such as images, music, and natural language, with the goal of making the content creation process more efficient and accessible. Large-scale models have become increasingly important in AIGC as they provide better intent extraction and generation results. This survey provides a comprehensive review of the history of generative models and recent advances in AIGC, focusing on both unimodal and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, the survey discusses the existing open problems and future challenges in AIGC. Overall, this survey serves as a valuable resource for individuals interested in understanding the background and secrets behind the impressive performance of AIGC techniques.

Federated learning, as one enabling technology of edge intelligence, has gained substantial attention due to its efficacy in training deep learning models without data privacy and network bandwidth concerns. However, due to the heterogeneity of the edge computing system and data, many methods suffer from the “ client-drift ” issue that could considerably impede the convergence of global model training: local models on clients can drift apart, and the aggregated model can be different from the global optimum. To tackle this issue, one intuitive idea is to guide the local model training by global teachers, i.e. , past global models, where each client learns the global knowledge from past global models via adaptive knowledge distillation techniques. Inspired by these insights, we propose a novel approach for heterogeneous federated learning, F ed GKD, which fuses the knowledge from historical global models and guides local training to alleviate the “ client-drift ” issue. In this paper, we evaluate F ed GKD through extensive experiments across various CV and NLP datasets (i.e., CIFAR-10/100, Tiny-ImageNet, AG News, SST5) under different heterogeneous settings. The proposed method is guaranteed to converge under common assumptions and outperforms the state-of-the-art baselines in the non-IID federated setting.