From f84f0781839c2ed8ce504f20c3eb00e6690b8953 Mon Sep 17 00:00:00 2001 From: Javier Date: Thu, 16 Nov 2023 13:24:25 +0000 Subject: [PATCH] Update FedBN baseline to new format (#2608) --- baselines/fedbn/.gitignore | 2 + baselines/fedbn/LICENSE | 202 ++++++++++++++ baselines/fedbn/README.md | 159 +++++++++++ baselines/fedbn/_static/train_loss.png | Bin 0 -> 52231 bytes baselines/fedbn/docs/multirun_plot.ipynb | 220 +++++++++++++++ baselines/fedbn/fedbn/__init__.py | 1 + baselines/fedbn/fedbn/client.py | 189 +++++++++++++ baselines/fedbn/fedbn/conf/base.yaml | 41 +++ baselines/fedbn/fedbn/conf/client/fedavg.yaml | 4 + baselines/fedbn/fedbn/conf/client/fedbn.yaml | 4 + baselines/fedbn/fedbn/dataset.py | 263 ++++++++++++++++++ baselines/fedbn/fedbn/dataset_preparation.py | 8 + baselines/fedbn/fedbn/main.py | 86 ++++++ baselines/fedbn/fedbn/models.py | 114 ++++++++ baselines/fedbn/fedbn/server.py | 5 + baselines/fedbn/fedbn/strategy.py | 85 ++++++ baselines/fedbn/fedbn/utils.py | 76 +++++ baselines/fedbn/pyproject.toml | 143 ++++++++++ doc/source/ref-changelog.md | 2 + 19 files changed, 1604 insertions(+) create mode 100644 baselines/fedbn/.gitignore create mode 100644 baselines/fedbn/LICENSE create mode 100644 baselines/fedbn/README.md create mode 100644 baselines/fedbn/_static/train_loss.png create mode 100644 baselines/fedbn/docs/multirun_plot.ipynb create mode 100644 baselines/fedbn/fedbn/__init__.py create mode 100644 baselines/fedbn/fedbn/client.py create mode 100644 baselines/fedbn/fedbn/conf/base.yaml create mode 100644 baselines/fedbn/fedbn/conf/client/fedavg.yaml create mode 100644 baselines/fedbn/fedbn/conf/client/fedbn.yaml create mode 100644 baselines/fedbn/fedbn/dataset.py create mode 100644 baselines/fedbn/fedbn/dataset_preparation.py create mode 100644 baselines/fedbn/fedbn/main.py create mode 100644 baselines/fedbn/fedbn/models.py create mode 100644 baselines/fedbn/fedbn/server.py create mode 100644 baselines/fedbn/fedbn/strategy.py create mode 100644 baselines/fedbn/fedbn/utils.py create mode 100644 baselines/fedbn/pyproject.toml diff --git a/baselines/fedbn/.gitignore b/baselines/fedbn/.gitignore new file mode 100644 index 000000000000..de1e160448e5 --- /dev/null +++ b/baselines/fedbn/.gitignore @@ -0,0 +1,2 @@ +outputs/ +multirun/ diff --git a/baselines/fedbn/LICENSE b/baselines/fedbn/LICENSE new file mode 100644 index 000000000000..d64569567334 --- /dev/null +++ b/baselines/fedbn/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/baselines/fedbn/README.md b/baselines/fedbn/README.md new file mode 100644 index 000000000000..cc4f68b90e9e --- /dev/null +++ b/baselines/fedbn/README.md @@ -0,0 +1,159 @@ +--- +title: "FedBN: Federated Learning on Non-IID Features via Local Batch Normalization" +url: https://arxiv.org/abs/2102.07623 +labels: [data heterogeneity, feature shift, cross-silo] +dataset: [MNIST, MNIST-M, SVHN, USPS, SynthDigits] +--- + +# FedBN: Federated Learning on Non-IID Features via Local Batch Normalization + +> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper. + + +**Paper:** [arxiv.org/abs/2102.07623](https://arxiv.org/abs/2102.07623) + +**Authors:** Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou + +**Abstract:** The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. Under this setting, neural network training performance may vary significantly according to the data distribution and even hurt training convergence. Most of the previous work has focused on a difference in the distribution of labels or client shifts. Unlike those settings, we address an important problem of FL, e.g., different scanners/sensors in medical imaging, different scenery distribution in autonomous driving (highway vs. city), where local clients store examples with different distributions compared to other clients, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg, as well as the state-of-the-art for non-iid data (FedProx) on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg. + + +## About this baseline + +**What’s implemented:** Figure 3 in the paper: convergence in training loss comparing `FedBN` to `FedAvg` for five datasets. + +**Datasets:** Vision datasets including digits 0-9. These datasets are: [MNIST](https://ieeexplore.ieee.org/document/726791), [MNIST-M](https://arxiv.org/pdf/1505.07818.pdf), [SVHN](http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf), [USPS](https://ieeexplore.ieee.org/document/291440), and [SynthDigits](https://arxiv.org/pdf/1505.07818.pdf). + +**Hardware Setup:** Using the default configurations, any machine with 8 CPU cores should be capable to run 100 rounds of FedAvg or FedBN in under 5 minutes. Therefore a GPU is not needed if you stick to the small model used in the paper and you limit clients to use a 10% of the data in each dataset (these are the default settings) + +**Contributors:** Meirui Jiang, Maria Boerner, Javier Fernandez-Marques + + +## Experimental Setup + +**Task:** Image classification + +**Model:** A six-layer CNN with 14,219,210 parameters following the structure described in appendix D.2. + +**Dataset:** This baseline makes use of the pre-processed partitions created and open source by the authors of the FedBN paper. You can read more about how those were created [here](https://github.com/med-air/FedBN). Follow the steps below in the `Environment Setup` section to download them. + + +A more detailed explanation of the datasets is given in the following table. + +| | MNIST | MNIST-M | SVHN | USPS | SynthDigits | +|--- |--- |--- |--- |--- |--- | +| data type| handwritten digits| MNIST modification randomly colored with colored patches| Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Syntehtic digits Windows TM font varying the orientation, blur and stroke colors | +| color | greyscale | RGB | RGB | greyscale | RGB | +| pixelsize | 28x28 | 28 x 28 | 32 x32 | 16 x16 | 32 x32 | +| labels | 0-9 | 0-9 | 1-10 | 0-9 | 1-10 | +| number of trainset | 60.000 | 60.000 | 73.257 | 9,298 | 50.000 | +| number of testset| 10.000 | 10.000 | 26.032 | - | - | +| image shape | (28,28) | (28,28,3) | (32,32,3) | (16,16) | (32,32,3) | + + +**Training Hyperparameters:** By default (i.e. if you don't override anything in the config) these main hyperparameters used are shown in the table below. For a complete list of hyperparameters, please refer to the config files in `fedbn/conf`. + +| Description | Value | +| ----------- | ----- | +| rounds | 10 | +| num_clients | 5 | +| strategy_fraction_fit | 1.0 | +| strategy.fraction_evaluate | 0.0 | +| training samples per client| 743 | +| lr | 10E-2 | +| local epochs | 1 | +| loss | cross entropy loss | +| optimizer | SGD | +| client_resources.num_cpu | 2 | +| client_resources.num_gpus | 0.0 | + +## Environment Setup + +To construct the Python environment, simply run: + +```bash +# Set directory to use python 3.10 (install with `pyenv install ` if you don't have it) +pyenv local 3.10.6 + +# Tell poetry to use python3.10 +poetry env use 3.10.6 + +# Install +poetry install +``` + +Before running the experiments you'll need to download the five datasets for this baseline. We'll be using the pre-processed datasets created by the `FedBN` authors. Download the dataset from [here](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155149226_link_cuhk_edu_hk/EV1YgHfFC4RKjw06NL4JMdgBMU21CegM12SpXrSmGjt3XA?e=XK2rFs) and move the file into a new directory named `data`. +```bash +mkdir data +mv data/ + +# now uncompress the zipfile +cd data && unzip digit_dataset.zip +cd data .. +``` + +## Running the Experiments + +First, activate your environment via `poetry shell`. The commands below show how to run the experiments and modify some of its key hyperparameters via the cli. Each time you run an experiment, the log and results will be stored inside `outputs//