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Add One-shot VFL project to the "research" folder (#1807)
* add research/oneshot-vfl * Update README.md * plot valid results * solve comments in pull request * clear all outputs in the notebook * delete _get_model() and redundant files * update README links and fix some formatting; use data splitting from VFL example --------- Co-authored-by: Holger Roth <[email protected]> Co-authored-by: Holger Roth <[email protected]>
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‎research/one-shot-vfl/README.md

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# One-shot VFL
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# One-shot Vertical Federated Learning with CIFAR-10
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This directory will contain the code for the methods described in
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This example includes instructions on how to run [one-shot vertical federated learning](https://arxiv.org/abs/2303.16270) using the
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CIFAR-10 dataset and the [FL simulator](https://nvflare.readthedocs.io/en/latest/user_guide/fl_simulator.html).
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### Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples ([arXiv:2303.16270](https://arxiv.org/abs/2303.16270))
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We assume one client holds the images, and the other client holds the labels to compute losses and accuracy metrics.
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Activations and corresponding gradients are being exchanged between the clients using NVFlare.
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###### Abstract:
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> Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) deals with scenarios in which the data on clients have different feature spaces but share some overlapping samples. Existing VFL approaches suffer from high communication costs and cannot deal efficiently with limited overlapping samples commonly seen in the real world. We propose a practical vertical federated learning (VFL) framework called **one-shot VFL** that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning. We also propose **few-shot VFL** to improve the accuracy further with just one more communication round between the server and the clients. In our proposed framework, the clients only need to communicate with the server once or only a few times. We evaluate the proposed VFL framework on both image and tabular datasets. Our methods can improve the accuracy by more than 46.5% and reduce the communication cost by more than 330 times compared with state-of-the-art VFL methods when evaluated on CIFAR-10.
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> Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) deals with scenarios in which the data on clients have different feature spaces but share some overlapping samples. Existing VFL approaches suffer from high communication costs and cannot deal efficiently with limited overlapping samples commonly seen in the real world. We propose a practical vertical federated learning (VFL) framework called **one-shot VFL** that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning. We also propose **few-shot VFL** to improve the accuracy further with just one more communication round between the server and the clients. In our proposed framework, the clients only need to communicate with the server once or only a few times. We evaluate the proposed VFL framework on both image and tabular datasets. Our methods can improve the accuracy by more than 46.5% and reduce the communication cost by more than 330× compared with state-of-the-art VFL methods when evaluated on CIFAR-10.
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<img src="./figs/oneshotVFL.png" alt="One-shot VFL setup" width="800"/>
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For instructions of how to run CIFAR-10 in real-world deployment settings,
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see the example on ["Real-world Federated Learning with CIFAR-10"](../../examples/advanced/cifar10/cifar10-real-world/README.md).
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## License
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- TBD
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The code in this directory is released under Apache v2 License.
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## 1. Setup
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This examples uses [JupyterLab](https://jupyter.org).
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## Citation
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We recommend creating a [virtual environment](../../examples/README.md#set-up-a-virtual-environment).
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> Sun, Jingwei, et al. "Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples." arXiv:2303.16270. 2023.
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## 2. Start JupyterLab
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To run the example, we recommend a GPU with at least 16 GB of memory.
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BibTeX
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Set `PYTHONPATH` to include custom files of this example and some reused files from the [CIFAR-10](../../examples/advanced/cifar10) examples:
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```
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export PYTHONPATH=${PWD}/src:${PWD}/../../examples/advanced/cifar10:${PWD}/../../examples/advanced/vertical_federated_learning/cifar10-splitnn/src
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```
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@misc{sun2023communicationefficient,
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title={Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples},
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author={Jingwei Sun and Ziyue Xu and Dong Yang and Vishwesh Nath and Wenqi Li and Can Zhao and Daguang Xu and Yiran Chen and Holger R. Roth},
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year={2023},
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eprint={2303.16270},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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Start Jupyter Lab
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```
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jupyter lab .
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```
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and open [cifar10_oneshot_vfl.ipynb](./cifar10_oneshot_vfl.ipynb).
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## 3. Example results
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An example local training curve with an overlap of 10,000 samples is shown below.
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One-shot VFL only requires the client to conduct two uploads and one download, which reduces the communication cost significantly. This CIFAR10 example can achieve a test accuracy of 79.0%, which is nearly the same as the results of vanilla [single-client VFL (split learning)](https://github.com/jeremy313/NVFlare/tree/dev/examples/advanced/vertical_federated_learning/cifar10-splitnn).
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<img src="./figs/oneshotVFL_results.png" alt="One-shot VFL results" width="600"/>

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