Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Auxo #237

Merged
merged 6 commits into from
Sep 23, 2023
Merged

Auxo #237

Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension


Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
52 changes: 52 additions & 0 deletions benchmark/configs/auxo/auxo.yml
Original file line number Diff line number Diff line change
@@ -0,0 +1,52 @@
# Configuration file of fed_hetero experiment

# ========== Cluster configuration ==========
# ip address of the parameter server (need 1 GPU process)
ps_ip: localhost
ps_port: 12345

# ip address of each worker:# of available gpus process on each gpu in this node
# Note that if we collocate ps and worker on same GPU, then we need to decrease this number of available processes on that GPU by 1
# E.g., master node has 4 available processes, then 1 for the ps, and worker should be set to: worker:3
worker_ips:
- localhost:[7,7,0,0] # worker_ip: [(# processes on gpu) for gpu in available_gpus] eg. 10.0.0.2:[4,4,4,4] This node has 4 gpus, each gpu has 4 processes.

exp_path: $FEDSCALE_HOME/examples/auxo

# Entry function of executor and aggregator under $exp_path
executor_entry: executor.py

aggregator_entry: aggregator.py

auth:
ssh_user: ""
ssh_private_key: ~/.ssh/id_rsa

# cmd to run before we can indeed run FAR (in order)
setup_commands:
- source $HOME/anaconda3/bin/activate fedscale

# ========== Additional job configuration ==========
# Default parameters are specified in config_parser.py, wherein more description of the parameter can be found

job_conf:
- job_name: auxo_femnist # Generate logs under this folder: log_path/job_name/time_stamp
- log_path: $FEDSCALE_HOME/benchmark # Path of log files
- num_participants: 200 # Number of participants per round, we use K=100 in our paper, large K will be much slower
- data_set: femnist # Dataset: openImg, google_speech, stackoverflow
- data_dir: $FEDSCALE_HOME/benchmark/dataset/data/ # Path of the dataset
- data_map_file: $FEDSCALE_HOME/benchmark/dataset/data/femnist/client_data_mapping/train.csv # Allocation of data to each client, turn to iid setting if not provided
- device_conf_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_device_capacity # Path of the client trace
- device_avail_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_behave_trace
- model: resnet18 # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
# - model_zoo: fedscale-torch-zoo
- eval_interval: 20 # How many rounds to run a testing on the testing set
- rounds: 1000 # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
- filter_less: 0 # Remove clients w/ less than 21 samples
- num_loaders: 2
- local_steps: 10
- learning_rate: 0.05
- batch_size: 20
- test_bsz: 20
- use_cuda: True
- save_checkpoint: False
29 changes: 29 additions & 0 deletions examples/auxo/Dockerfile
Original file line number Diff line number Diff line change
@@ -0,0 +1,29 @@
# Use an official CUDA image as a parent image
FROM nvidia/cuda:11.0-base-ubuntu20.04

# Set the working directory inside the container
WORKDIR /app

# Install necessary system packages
RUN apt-get update && apt-get install -y python3.7 python3-pip

# Create a virtual environment and activate it
RUN python3.7 -m pip install virtualenv
RUN python3.7 -m virtualenv venv
RUN /bin/bash -c "source venv/bin/activate"

# Copy the requirements file into the container
COPY requirements.txt .

# Install the Python dependencies
RUN pip install --upgrade pip && pip install -r requirements.txt

# Copy the project files into the container (assuming your project is in the current directory)
COPY . .

# Install your project using pip
RUN pip install -e .

# Command to run when the container starts
CMD ["bash"]

72 changes: 72 additions & 0 deletions examples/auxo/README.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,72 @@


<div align="center">
<picture>
<img alt="Auxo logo" width="45%" src="fig/auxo.png">
</picture>
<h1>Auxo: Efficient Federated Learning via Scalable Client Clustering</h1>

</div>

Auxo is a heterogeneity manager in Federated Learning (FL) through scalable and efficient cohort-based training mechanisms.
For more details, refer to our academic paper on SoCC'23 [paper](https://arxiv.org/abs/2210.16656).


## Key Features

- **Scalable Cohort Identification**: Efficiently identifies cohorts even in large-scale FL deployments.

- **Cohort-Based Training**: Optimizes the performance of existing FL algorithms by reducing intra-cohort heterogeneity.

- **Resource Efficiency**: Designed to work in low-availability, resource-constrained settings without additional computational overhead.

- **Privacy Preservation**: Respects user privacy by avoiding the need for traditional clustering methods that require access to client data.


## Getting Started
### Install
Following the installation steps if you have not installed fedscale yet.
```commandline
docker build -t fedscale:auxo .
docker run --gpus all -it --name auxo -v $FEDSCALE_HOME:/workspace/FedScale fedscale:auxo /bin/bash
```

```
echo export FEDSCALE_HOME=$(pwd) >> ~/.bashrc
echo alias fedscale=\'bash ${FEDSCALE_HOME}/fedscale.sh\' >> ~/.bashrc
source ~/.bashrc
```

### Prepare dataset
After setting up the fedscale environment, you can download the dataset and partition each client dataset into train set and test set.

```commandline
fedscale dataset download femnist
cd $FEDSCALE_HOME/examples/auxo
python -m utils.prepare_test_train ../../benchmark/dataset/data/femnist/client_data_mapping/train.csv
python -m utils.prepare_test_train ../../benchmark/dataset/data/femnist/client_data_mapping/test.csv
python -m utils.prepare_test_train ../../benchmark/dataset/data/femnist/client_data_mapping/val.csv
```
### Run Auxo
```
cd $FEDSCALE_HOME
fedscale driver start benchmark/configs/auxo/auxo.yml
```

### Visualize continuous clustering algorithm
```commandline
cd $FEDSCALE_HOME/examples/auxo
python playground.py
```
Visualized clustering Results:

<p float="left">
<img src="fig/epoch_14.png" width="150" />
<img src="fig/epoch_100.png" width="150" />
<img src="fig/epoch_224.png" width="150" />
<img src="fig/epoch_300.png" width="150" />
<img src="fig/epoch_500.png" width="150" />
<img src="fig/epoch_700.png" width="150" />
</p>


Loading
Loading