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Mobile edge Deep learning Frame-Work for model training, federate learning

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Hail-cali/pinME

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Personalized image net based on Mobile-Edge

Frame work for fedearte learning on mobile edge

frame-work_fed

frame-work arc

frame-work


how to run

check port forwarding first

  1. run server :: preset epoch, client_k which you want aggregate some
  2. run client :: preset epoch same as server, gpu num which you want to put in (if can't, switched to cpu )
  • Server

  • DEV
  • new docker container init
  • will be added with new dockerfile
  • dockerfile-build
docker build -t hail/pinme/server:1.5 server/

IMPORTANT !! YOU SHOULD DUMP CONTAINER WITH SECRET

  • dump sectet
docker commit --change "ENV SERVER_POT=0000"
  • dockerfile -run
docker run -d --name pinme.server hail/pinme/server:1.5
  • shell
source server/start.sh
  • python directly
python run_server.py  --SERVER_PORT 59919 --SERVER_HOST '127.0.0.2' --model fedavg --k_clients 1
  • Client

  • new dockerfile
docker build -t hail/pinme/client:1.5 client/
  • dockerfile -run
docker run -d --name pinme.client1 hail/pinme/client:1.5 
  • shell
source clinet/start.sh
  • python cmd
python run_client.py --CLIENT_PORT 59919 --CLIENT_HOST '127.0.0.2' --model fedavg --n_epochs 1 --gpu 0
  • if you want to use server & edge, not locally, then set server's port & host to fit in docker forwarding setting

baseline docker image link

for docker setting guide

base dataset link


Architecture Description

  • communicate : communicate stream & copy stream
  • models : model architecture & federate learning
  • worker : data & model & network setter, data loader
  • fed : frame-work hugging phase
  • server : server class & run script
  • clinet : client class & run script
  • utils : debug, plot, logger

Citation


@software{pinME,
  author = {Hail},
  month = {12},
  title = {{Frame-work for federate learning}},
  url = {https://github.com/Hail-cali/pinME},
  version = {1.2.0},
  year = {2021}
}

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Mobile edge Deep learning Frame-Work for model training, federate learning

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