Refresh FedProx MNIST baseline

baselines/fedprox/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.

baselines/fedprox/README.md

@@ -0,0 +1,109 @@
+---
+title: Federated Optimization in Heterogeneous Networks
+url: https://arxiv.org/abs/1812.06127
+labels: [image classification, cross-device, stragglers] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo")
+dataset: [mnist] # list of datasets you include in your baseline
+---
+
+# FedProx: Federated Optimization in Heterogeneous Networks
+
+> 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:** https://arxiv.org/abs/1812.06127
+
+**Authors:** Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar and Virginia Smith.
+
+**Abstract:** Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity). In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re-parametrization of FedAvg, the current state-of-the-art method for federated learning. While this re-parameterization makes only minor modifications to the method itself, these modifications have important ramifications both in theory and in practice. Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity). Practically, we demonstrate that FedProx allows for more robust convergence than FedAvg across a suite of realistic federated datasets. In particular, in highly heterogeneous settings, FedProx demonstrates significantly more stable and accurate convergence behavior relative to FedAvg---improving absolute test accuracy by 22% on average.
+
+
+## About this baseline
+**What's implemented:** The code in this directory replicates the experiments in *Federated Optimization in Heterogeneous Networks* (Li et al., 2018) for MNIST, which proposed the FedProx algorithm. Concretely, it replicates the results for MNIST in Figure 1 and 7.
+
+**Datasets:** MNIST from PyTorch's Torchvision
+
+**Hardware Setup:** These experiments were run on a desktop machine with 24 CPU threads. Any machine with 4 CPU cores or more would be able to run it in a reasonable amount of time. Note: we install PyTorch with GPU support but by default, the entire experiment runs on CPU-only mode.
+
+**Contributors:** Charles Beauville and Javier Fernandez-Marques
+
+
+## Experimental Setup
+
+**Task:** Image classification
+
+**Model:** This directory implements two models:
+* A logistic regression model used in the FedProx paper for MNIST (see `models/LogisticRegression`). This is the model used by default.
+* A two-layer CNN network as used in the FedAvg paper (see `models/Net`)
+
+**Dataset:** This baseline only includes the MNIST dataset. By default it will be partitioned into 1000 clients following a pathological split where each client has examples of two (out of ten) class labels. The number of examples in each client is derived by sampling from a powerlaw distribution. The settings are as follow:
+
+| Dataset | #classes | #partitions | partitioning method | partition settings |
+| :------ | :---: | :---: | :---: | :---: |
+| MNIST | 10 | 1000 | pathological with power law | 2 classes per client |
+
+**Training Hyperparameters:**
+The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py` directly)
+
+| Description | Default Value |
+| ----------- | ----- |
+| total clients | 1000 |
+| clients per round | 10 |
+| number of rounds | 100 |
+| client resources | {'num_cpus': 2.0, 'num_gpus': 0.0 }|
+| data partition | pathological with power law (2 classes per client) |
+| optimizer | SGD with proximal term |
+| proximal mu | 1.0 |
+| stragglers_fraction | 0.9 |
+
+## Environment Setup
+
+To construct the Python environment follow these steps:
+
+```bash
+# install the base Poetry environment
+poetry install
+
+# activate the environment
+poetry shell
+
+# install PyTorch with GPU support. Please note this baseline is very lightweight so it can run fine on a CPU.
+pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
+```
+
+## Running the Experiments
+
+To run this FedProx with MNIST baseline, first ensure you have activated your Poetry environment (execute `poetry shell` from this directory), then:
+
+```bash
+python -m fedprox.main # this will run using the default settings in the `conf/config.yaml`
+
+# you can override settings directly from the command line
+python -m fedprox.main mu=1 num_rounds=200 # will set proximal mu to 1 and the number of rounds to 200
+
+# if you run this baseline with a larger model, you might want to use the GPU (not used by default).
+# you can enable this by overriding the `server_device` and `client_resources` config. For example
+# the below will run the server model on the GPU and 4 clients will be allowed to run concurrently on a GPU (assuming you also meet the CPU criteria for clients)
+python -m fedprox.main server_device=cuda client_resources.num_gpus=0.25
+```
+
+To run using FedAvg:
+```bash
+# this will use a variation of FedAvg that drops the clients that were flagged as stragglers
+# This is done so to match the experimental setup in the FedProx paper
+python -m fedprox.main --config-name fedavg
+
+# this config can also be overriden from the CLI
+```
+
+## Expected results
+
+With the following command we run both FedProx and FedAvg configurations while iterating through different values of `mu` and `stragglers_fraction`. We ran each experiment five times (this is achieved by artificially adding an extra element to the config but that it doesn't have an impact on the FL setting `'+repeat_num=range(5)'`)
+
+```bash
+python -m fedprox.main --multirun mu=0.0,2.0 stragglers_fraction=0.0,0.5,0.9 '+repeat_num=range(5)'
+# note that for FedAvg we don't want to change the proximal term mu since it should be kept at 0.0
+python -m fedprox.main --config-name fedavg --multirun stragglers_fraction=0.0,0.5,0.9 '+repeat_num=range(5)'
+```
+
+The above commands would generate results that you can plot and would look like:
+
+![](docs/FedProx_mnist.png)