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

2025-03-26

AI Summary

To address slow convergence and accuracy collapse in federated learning (FL) caused by non-independent and identically distributed (Non-IID) data, this paper proposes an AIGC-enhanced generative federated learning (GenFL) framework. GenFL is the first to integrate diffusion models into local FL clients for lightweight, privacy-preserving synthetic data generation. It introduces a generative aggregation mechanism and a dynamic weighting strategy specifically designed for Non-IID settings, jointly optimizing data distribution alignment and knowledge synergistic enhancement. Extensive experiments on CIFAR-10 and CIFAR-100 under standard Non-IID configurations demonstrate that GenFL improves test accuracy by 8.2–12.7%, while substantially mitigating accuracy degradation and convergence instability. The framework establishes a scalable, robust paradigm for heterogeneous FL in edge intelligence scenarios.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Addressing data heterogeneity in Federated Learning using AIGC
• Proposing GenFL architecture to improve FL performance
• Overcoming FL bottlenecks with synthetic data generation

Innovation

• AIGC-assisted Federated Learning architecture
• Generative FL with diffusion models
• Overcoming data heterogeneity in FL