Summary of Towards Efficient Model-heterogeneity Federated Learning For Large Models, by Ruofan Jia et al.
Towards Efficient Model-Heterogeneity Federated Learning for Large Models
by Ruofan Jia, Weiying Xie, Jie Lei, Haonan Qin, Jitao Ma, Leyuan Fang
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 The proposed HeteroTune framework tackles the challenges of deploying large models in federated learning for edge computing applications. The framework introduces a novel parameter-efficient fine-tuning structure called FedAdapter, which enables efficient knowledge aggregation across diverse models. By reducing computational and communication overhead, HeteroTune achieves state-of-the-art results on computer vision (CV) and natural language processing (NLP) tasks while maintaining efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to use large models in edge computing. This is important because these models can do many things well, but they need to be adapted for different devices with limited resources. The authors create a framework called HeteroTune that helps make this happen. They also invent a new way to fine-tune the models, called FedAdapter, which makes it more efficient. This means that the models can learn from each other without using too much energy or data. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Natural language processing » Nlp » Parameter efficient
