Zeno

This is the python implementation of the paper "Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance"

Requirements

The following python packages needs to be installed by pip:

1. MXNET (we use GPU, thus mxnet-cu80 is preferred)
2. Gluon-CV
3. Numpy

The users can simply run the following commond in their own virtualenv:

pip install --no-cache-dir numpy mxnet-cu80 gluoncv

Run the demo

Options:

Option	Desctiption
--batch_size 100	batch size of the workers
--lr 0.1	learning rate
--nworkers 20	number of workers
--nepochs 200	total number of epochs
--gpu	index of GPU to be used
--nbyz	number of faulty workers
--byz_type	type of failures, bitflip or labelflip
--aggregation	aggregation method, mean, median, krum, or zeno
--zeno_size 4	batch size of Zeno, $n_r$ in the paper
--rho_ratio	in the paper, $\rho = \gamma / rho\_ratio$
--b	number of trimmed values, $b$ in the paper
--iid 1	-iid 1 means the wokers are training on IID data
--interval 10	log interval
--seed 337	random seed

• Train with 20 workers, 8 of them are faulty with bit-flipping failures, Zeno as aggregation:

python mxnet_cnn_cifar10_impl.py --gpu 0 --nepochs 200 --lr 0.05 --batch_size 100 --nworkers 20 --nbyz 8 --byz_type bitflip --rho 200 --b 12 --zeno_size 4 --aggregation zeno

More detailed commands/instructions can be found in the demo script test_zeno_1.sh