Dataset: Google Landmark, COCO, ImageNet
Model: EfficientNetB0, MobileNetV3
Dataset: COCO
Model: YoLoV5
Google Doc: https://docs.google.com/document/d/1AU-3XT5vLKjLjvOOcdfPfTDwnww1C3xEaroA94pKaWU/edit#heading=h.xldeyzrvdr99
Dataset: COCO (Pretraining), Pascal (Fine-Tuning)
Model: DeepLabV3+, U-Net
https://docs.google.com/document/d/1TJi3os3oRQlm6rIwoYfHjUA80M_9IQZ0_iRApuRs4s8/edit
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
-
FedML: a soft repository link generated usinggit submodule add https://github.com/FedML-AI/FedML. -
data: provide data downloading scripts and store the downloaded datasets. Note that inFedML/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 owntrainer.pyby inheriting the base class inFedML/fedml-core/trainer/fedavg_trainer.py. Some tasks can share the same trainer. -
experiments/distributed:
experimentsis the entry point for training. It contains experiments in different platforms. We start fromdistributed.- Every experiment integrates FOUR building blocks
FedML(federated optimizers),data_preprocessing,model,trainer. - To develop new experiments, please refer the code at
experiments/distributed/text-classification.
experiments/centralized:
- please provide centralized training script in this directory.
- This is used to get the reference model accuracy for FL.
- 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.
cd FedML
git checkout master && git pull
cd ..
git add FedML
git commit -m "#<issue_id> - updating submodule FedML to latest"
git push