Summary of Mllm-llava-fl: Multimodal Large Language Model Assisted Federated Learning, by Jianyi Zhang et al.
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
by Jianyi Zhang, Hao Frank Yang, Ang Li, Xin Guo, Pu Wang, Haiming Wang, Yiran Chen, Hai Li
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 paper introduces a novel federated learning framework called Multimodal Large Language Model Assisted Federated Learning (MLLM-LLaVA-FL) that leverages powerful multimodal large language models (MLLMs) to address the challenges of data heterogeneity and long-tailed distribution in federated learning. The framework employs MLLMs at the server end to pretrain a model, distribute it among clients for local training, and then align the locally trained models under MLLM supervision. Experimental evaluations on established benchmarks show promising performance in scenarios with data heterogeneity and long-tail distribution across different clients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make machine learning work better when different devices have different types of data. It uses special language models that can understand many different kinds of information, like text and images. This helps the model learn more accurately from all the different devices. The approach is called federated learning, and it’s important for making sure devices don’t need to share their data directly with each other. The paper shows that this method works well in situations where the devices have very different kinds of information. |
Keywords
» Artificial intelligence » Federated learning » Large language model » Machine learning
