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AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions

arXiv 2503.20166 ↗First posted 26 Mar 2025cs.LG cs.DCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 26 Mar 2025.

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References · 9

  1. 2018Federated Learning with Non-IID Datacited by 1,108
  2. 2020Federated Learning with Matched Averagingcited by 564
  3. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  4. 2021On Large-Cohort Training for Federated Learningcited by 74
  5. 2019Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learningcited by 20
  6. 2023Feature Matching Data Synthesis for Non-IID Federated Learningcited by 13
  7. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  8. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Datacited by 11
  9. 2024IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Contentcited by 3

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  1. 2023Federated Learning-Empowered AI-Generated Content in Wireless Networks
  2. 2025Edge-Assisted Collaborative Fine-Tuning for Multi-User Personalized Artificial Intelligence Generated Content (AIGC)
  3. 2021An Optimization Framework for Federated Edge Learning
  4. 2021Optimization-Based GenQSGD for Federated Edge Learning
  5. 2024Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks
  6. 2025Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems
  7. 2022HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
  8. 2022Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion