A Proof-of-Stake (PoS) blockchain-based Federated Learning framework with a validation scheme robust against the distorted local model updates. This repo hosts an simulated implementation for VBFL written in Python.
python 3.7.6
pytorch 1.4.0
(1) Clone the repo
$ git clone https://github.com/aayushdewangan11/Bloackchain_Enabled_Federated-learning.git
(2) Create a new conda environment with python 3.7.6
$ conda create -n VBFL python=3.7.6
$ conda activate VBFL
(3) Head to https://pytorch.org/ for instructions to install PyTorch
$ # If using CUDA, first check its version
$ nvidia-smi
$ # Install the correct CUDA version for PyTorch.
$ # The code was tested on CUDA 10.1 and PyTorch 1.4.0
$ conda install pytorch=1.4.0 torchvision torchaudio cudatoolkit=10.1 -c pytorch
(4) Install pycryptodome 3.9.9 and matplotlib
$ conda install pycryptodome=3.9.9
$ conda install matplotlib
Sample running command
$ python main.py -nd 20 -max_ncomm 100 -ha 12,5,3 -aio 1 -pow 0 -ko 6 -nm 3 -vh 0.08 -cs 0 -B 10 -mn mnist_cnn -iid 0 -lr 0.01 -dtx 1
VBFL arguments
(1) -nd 20: 20 devices.
(2) -max_ncomm 100: maximum 100 communication rounds.
(3) -ha 12,5,3: role assignment hard-assigned to 12 workers, 5 validators and 3 miners for each communication round. A * in -ha means the corresponding number of roles are not limited. e.g., -ha *,5,* means at least 5 validators would be assigned in each communication round, and the rest of the devices are dynamically and randomly assigned to any role. -ha *,*,* means the role-assigning in each communication round is completely dynamic and random.
(4) -aio 1: aio means "all in one network", namely, every device in the emulation has every other device in its peer list. This is to simulate that VBFL runs on a permissioned blockchain. If using -aio 0, the emulation will let a device (registrant) randomly register with another device (register) and copy the register's peer list.
(5) -pow 0: the argument of -pow specifies the proof-of-work difficulty. When using 0, VBFL runs with VBFL-PoS consensus to select the winning miner.
(6) -ko 6: this argument means a device is blacklisted after it is identified as malicious after 6 consecutive rounds as a worker.
(7) -nm 3: exactly 3 devices will be malicious nodes.
(8) -vh 0.08: validator-threshold is set to 0.08 for all communication rounds. This value may be adaptively learned by validators in a future version.
(9) -cs 0: as the emulation does not include mechanisms to disturb digital signature of the transactions, this argument turns off signature checking to speed up the execution.
Federated Learning arguments (inherited from https://github.com/WHDY/FedAvg)
(10) -B 10: batch size set to 10.
(11) -mn mnist_cnn: use mnist_cnn model. Another choice is mnist_2nn, or you may put your own network inside of Models.py and specify it.
(12) -iid 0: shard the training data set in Non-IID way.
(13) -lr 0.01: learning rate set to 0.01.
Other arguments
(14) -dtx 1: see Known Issue.
Please see main.py for other argument options.
Please raise other issues and concerns you found. Thank you!
