FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

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Deep Learning FedCV
Overview

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
Comments
  • Refactored pascal and coco segmentation dataloaders to reuse noniid_partition code from fedml_core

    Refactored pascal and coco segmentation dataloaders to reuse noniid_partition code from fedml_core

    This concludes the noniid_partition 'refactoring for reuse' task

    Corresponding FedML file: https://github.com/FedML-AI/FedML/blob/fedseg/fedml_core/non_iid_partition/noniid_partition.py

    dataloader transplant 
    opened by mitashimpi 1
  • Model refactor to remove FedML syspath anti pattern

    Model refactor to remove FedML syspath anti pattern

    +1 @keerti-1997 @mitashimpi

    I have removed the dependency on FedML sys path now it will always refer the submodule in our repo. Also I have removed AlignedXception code as it's not present and have renamed BatchNorm to self.BatchNorm for naming conventions.

    enhancement 
    opened by sadarshannaiynar 0
  • MobileNet V2 Implementation for segmentation

    MobileNet V2 Implementation for segmentation

    +1 @keerti-1997 @mitashimpi

    The implementation was taken from https://github.com/VainF/DeepLabV3Plus-Pytorch and made slight structural modifications to suit our architecture.

    enhancement 
    opened by sadarshannaiynar 0
  • Question about missing files

    Question about missing files

    Hello, I would like to ask when I run the code in /distributed/detection and find that the imported package in main_fedavg_yolo.py does not actually exist. For instance, the files in the model/detection/utils directory are inconsistent with the actual imported files. I hope my question is answered, thanks a lot!

    opened by Yohuna 3
  • Problems of distributed computing in federated learning

    Problems of distributed computing in federated learning

    When using distributed operation, I have four Gpus, each of which has a client. During the training process, each GPU has a huge difference. Two gpus even ran out of memory. By the way, I also found that gpu training with overflow was extremely slow and seemed to have gpu utilization close to zero.

    opened by rG223 1
  • Is that possible to run Fed-CV on Jetson Nano?

    Is that possible to run Fed-CV on Jetson Nano?

    Hi there Really appreciate you guys provide the opensource Fed-CV, and help us to research on this framework. As we focus on object detection, is that possible to run Fed-CV on Jetson Nano?

    opened by lancasterJie 0
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FedML-AI
FedML: A Research Library and Benchmark for Federated Machine Learning
FedML-AI
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