AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions
This work addresses data heterogeneity in federated learning for edge intelligence, but it appears incremental as it builds on existing AIGC techniques.
The paper tackles the performance limitations of federated learning due to data heterogeneity by proposing a Generative Federated Learning (GenFL) architecture that uses AIGC and diffusion models to generate synthetic data, showing effectiveness in experiments on CIFAR10 and CIFAR100 datasets under non-IID conditions.
Federated learning (FL) can fully leverage large-scale terminal data while ensuring privacy and security, and is considered as a distributed alternative for the centralized machine learning. However, the issue of data heterogeneity poses limitations on FL's performance. To address this challenge, artificial intelligence-generated content (AIGC) which is an innovative data synthesis technique emerges as one potential solution. In this article, we first provide an overview of the system architecture, performance metrics, and challenges associated with AIGC-assistant FL system design. We then propose the Generative federated learning (GenFL) architecture and present its workflow, including the design of aggregation and weight policy. Finally, using the CIFAR10 and CIFAR100 datasets, we employ diffusion models to generate dataset and improve FL performance. Experiments conducted under various non-independent and identically distributed (non-IID) data distributions demonstrate the effectiveness of GenFL on overcoming the bottlenecks in FL caused by data heterogeneity. Open research directions in the research of AIGC-assisted FL are also discussed.