FengHZ/BAFFLE

The official implement of paper "Does Federated Learning Really Need Backpropagation?"

Does Federated Learning Really Need Backpropagation?

Here is the official implementation of the model BAFFLE in paper Does Federated Learning Really Need Backpropagation?

Setup

Install Package Dependencies

python >= 3.6
torch >= 1.2.0
torchvision >= 0.4.0
wandb
ast
torch_ema

BAFFLE For MNIST with IID Participation ($C=10,\sigma=10^{-4}$)

Evaluate BAFFLE with Different K

# For LeNet
python main.py --K 100 --dataset MNIST --epochs 20 --lr 1e-2 --optimizer Adam --ema --normalization NoNorm 
# For WRN-10-2
python main.py --K 100 --net-name wideresnet --depth 10 --width 2 --dataset MNIST --epochs 20 --lr 1e-2 --optimizer Adam  --ema

Evaluate BAFFLE with Different Guidelines

# w/o EMA
python main.py --K 100 --dataset MNIST --epochs 20 --lr 1e-2 --optimizer Adam --normalization NoNorm
# ReLU 
python main.py --K 100 --dataset MNIST --epochs 20 --lr 1e-2 --optimizer Adam --normalization NoNorm --ema --activation "ReLU"
# SELU
python main.py --K 100 --dataset MNIST --epochs 20 --lr 1e-2 --optimizer Adam --normalization NoNorm --ema --activation "SELU"
# Central Scheme
python main.py --K 50 --fd-format center  --dataset MNIST --epochs 20 --lr 1e-2 --optimizer Adam  --normalization NoNorm --ema 

Evaluate BAFFLE with Epoch-level Communications

# One Epoch Communication
python main.py --K 100 --fedopt FedAvg --dataset MNIST --epochs 20 --lr 1e-2 --optimizer Adam  --ema --normalization NoNorm