Summary of Fedhpl: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation, by Yuting Ma et al.
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
by Yuting Ma, Lechao Cheng, Yaxiong Wang, Zhun Zhong, Xiaohua Xu, Meng Wang
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Medium Difficulty summary: Federated learning (FL) is a privacy-preserving paradigm that enables clients to train models while keeping raw data locally. However, existing methods struggle with heterogeneous challenges such as distinct model architectures, varying data distributions, and limited resources across local clients. To overcome these limitations, we propose FedHPL, a unified FL framework based on prompt tuning and logit distillation. Our approach leverages learnable visual prompts to fine-tune pre-trained foundation models for downstream tasks, accelerating training and improving performance under resource constraints. Additionally, our global logit distillation scheme handles model heterogeneity by aggregating local knowledge. We provide theoretical guarantees on the generalization error bound and demonstrate FedHPL outperforms state-of-the-art FL approaches with reduced computation overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a way to train models together while keeping private data safe at home. This is called federated learning (FL). But, this method has some big challenges. Sometimes, different models work better on different types of data, or the computer at home doesn’t have enough power to train fast. To solve these problems, we created a new way to do FL called FedHPL. It uses special prompts to make sure the model is good for each type of task and also helps different models work together smoothly. We tested our method on many datasets and it worked better than other methods with less effort. |
Keywords
» Artificial intelligence » Distillation » Federated learning » Generalization » Prompt
