Summary of Gefl: Model-agnostic Federated Learning with Generative Models, by Honggu Kang et al.
GeFL: Model-Agnostic Federated Learning with Generative Models
by Honggu Kang, Seohyeon Cha, Joonhyuk Kang
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackle the challenge of federated learning (FL) with heterogeneous models, where users have different computing capabilities and network bandwidths. The proposed Generative Model-Aided Federated Learning (GeFL) framework aggregates global knowledge across users with diverse models, leading to notable performance improvements on various classification tasks. However, concerns about privacy and scalability remain. To address these issues, the authors introduce GeFL-F, a novel framework that trains target networks aided by feature-generative models, achieving consistent performance gains while preserving better privacy and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making it easier for many devices to work together to learn new things without sharing their private information. The team came up with a way to do this called GeFL, which helps devices with different abilities share knowledge and get better at tasks like classification. They tested GeFL on several tasks and found that it works well, but they also realized there are some problems to fix, like keeping the devices’ personal info safe and making sure it all runs smoothly. |
Keywords
» Artificial intelligence » Classification » Federated learning » Generative model
