zj-jayzhang/FedNTD

(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"

FedNTD (Federated Not-True Distillation)

This repository is the official PyTorch implementation of:

"Preservation of Global Knowledge by Not-True Distillation in Federated Learning (NeurIPS 2022)".

Our code structure is based on FedML but refactored for simplicity.

We implemented various FL algorithms in our framework:

• FedCurv, FedAvgM, Scaffold, Fedprox, MOON, FedNova, FedDyn, and FedNTD (Ours).

Requirements

• This codebase is written for python3 (used python 3.8.8 while implementing).
• We use Pytorch version of 1.9.0 and 10.2, 11.0 CUDA version.
• To install necessary python packages,

  pip install -r requirements.txt
  

• The logs are uploaded to the wandb server. If you do not have a wandb account, just install and use as offline mode.

  pip install wandb
  wandb off
  

How to Run Codes?

The configuration skeleton for each algorithm is in ./config/*.json.

• python ./main.py --config_path ./config/algorithm_name.json conducts the experiment with the default setups.

There are two ways to change the configurations:

1. Change (or Write a new one) the configuration file in ./config directory with the above command.
2. Use parser arguments to overload the configuration file.

• --dataset_name: name of the datasets (e.g., mnist, cifar10, cifar100 or cinic10).
  • for cinic-10 datasets, the data should be downloaded first using ./data/cinic10/download.sh.
• --n_clients: the number of total clients (default: 100).
• --batch_size: the size of batch to be used for local training. (default: 50)
• --partition_method: non-IID partition strategy (e.g. sharding, lda).
• --partition_s: shard per user (only for sharding).
• --partition_alpha: concentration parameter alpha for latent Dirichlet Allocation (only for lda).
• --model_name: model architecture to be used (e.g., fedavg_mnist, fedavg_cifar, or mobile).
• --n_rounds: the number of total communication rounds. (default: 200)
• --sample_ratio: fraction of clients to be ramdonly sampled at each round (default: 0.1)
• --local_epochs: the number of local epochs (default: 5).
• --lr: the initial learning rate for local training (default: 0.01)
• --momentum: the momentum for SGD (default: 0.9).
• --wd: weight decay for optimization (default: 1e-5)
• --algo_name: algorithm name of the experiment (e.g., fedavg, fedntd)
• --seed: random seed

Reference Github

We refer to the following repositories:

• https://github.com/FedML-AI/FedML (For overall code structure)
• https://github.com/JYWa/FedNova
• https://github.com/QinbinLi/MOON
• https://github.com/BayesWatch/cinic-10

Citing this work

@article{lee2021preservation,
  title={Preservation of the global knowledge by not-true self knowledge distillation in federated learning},
  author={Lee, Gihun and Shin, Yongjin and Jeong, Minchan and Yun, Se-Young},
  journal={arXiv preprint arXiv:2106.03097},
  year={2021}
}