kunal266 / FedCV

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

Image Classification

Dataset: Google Landmark, COCO, ImageNet

Model: EfficientNetB0, MobileNetV3

Object Detection

Dataset: COCO

Model: YoLoV5

Google Doc: https://docs.google.com/document/d/1AU-3XT5vLKjLjvOOcdfPfTDwnww1C3xEaroA94pKaWU/edit#heading=h.xldeyzrvdr99

Image Segmentation

Dataset: COCO (Pretraining), Pascal (Fine-Tuning)

Model: DeepLabV3+, U-Net

https://docs.google.com/document/d/1TJi3os3oRQlm6rIwoYfHjUA80M_9IQZ0_iRApuRs4s8/edit

Installation

http://doc.fedml.ai/#/installation

After the clone of this repository, please run the following command to get FedML submodule to your local.

mkdir FedML
cd FedML
git submodule init
git submodule update

Code Structure of FedCV

• FedML: a soft repository link generated using git submodule add https://github.com/FedML-AI/FedML.

• data: provide data downloading scripts and store the downloaded datasets. Note that in FedML/data, there also exists datasets for research, but these datasets are used for evaluating federated optimizers (e.g., FedAvg) and platforms. FedNLP supports more advanced datasets and models.

• data_preprocessing: data loaders

• model: advanced CV models.

• trainer: please define your own trainer.py by inheriting the base class in FedML/fedml-core/trainer/fedavg_trainer.py. Some tasks can share the same trainer.

• experiments/distributed:

1. experiments is the entry point for training. It contains experiments in different platforms. We start from distributed.
2. Every experiment integrates FOUR building blocks FedML (federated optimizers), data_preprocessing, model, trainer.
3. To develop new experiments, please refer the code at experiments/distributed/text-classification.

• experiments/centralized:

1. please provide centralized training script in this directory.
2. This is used to get the reference model accuracy for FL.
3. You may need to accelerate your training through distributed training on multi-GPUs and multi-machines. Please refer the code at experiments/centralized/DDP_demo.

Update FedML Submodule

cd FedML
git checkout master && git pull
cd ..
git add FedML
git commit -m "#<issue_id> - updating submodule FedML to latest"
git push