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


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


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 --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

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