Summary of Imfl-aigc: Incentive Mechanism Design For Federated Learning Empowered by Artificial Intelligence Generated Content, By Guangjing Huang et al.
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content
by Guangjing Huang, Qiong Wu, Jingyi Li, Xu Chen
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore ways to enhance federated learning (FL) model performance using artificial intelligence-generated content (AIGC). They propose a data quality assessment method and analyze the convergence performance of FL models trained on authentic and AI-generated data. To address the issue of clients being reluctant to participate without economic incentives, they develop a data quality-aware incentive mechanism that minimizes server costs while maintaining high training accuracy. The proposed mechanism is tested with real-world datasets and outperforms existing benchmarks by reducing server costs up to 53.34%. This work sheds light on the critical issue of enabling AIGC-empowered FL and paves the way for its practical application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning (FL) allows devices to work together without sharing their data. This paper looks at how to make FL better by using fake data created by AI. The authors propose a new way to check if the fake data is good quality and analyze how well FL models perform with this blended data. They also create an incentive system that encourages devices to participate, taking into account their private information. The results show that this system works better than others, reducing costs for the server while maintaining high accuracy. |
Keywords
» Artificial intelligence » Federated learning
