[ICDE2022] This code is the implementation of FedADMM

FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity

Author: Yonghai Gong, Yichuan Li, and Nikolaos M. Freris

The code is modified from https://github.com/AshwinRJ/Federated-Learning-PyTorch

Requirments

Install all the packages from requirments.txt

• python = 3.8.5
• pytorch = 1.9.0
• torchvision = 0.10.0
• numpy = 1.19.2
• tensorboardx = 2.2
• matplotlib = 3.3.2

Datasets and Models

Model	# of para	Dataset	Target Accuracy
CNN 1	1,663,370	MNIST	97%
CNN 1	1,663,370	FMNIST	80%
CNN 2	1,105,098	CIFAR-10	45%

Dataset Distribution

• IID: data are evenly distributed to clients
• non-IID: first arrange the training data by label and then distribute them into shards. Each client is assigned two shards randomly.

Options

The default values for various parameters parsed to the experiment are given in options.py. Details are given on some of those parameters:

• --num_users: number of users: m
• --frac: the fraction of clients: C
• --epochs: number of global rounds of training
• --local_ep: the number of local epochs: E
• --local_bs: local batch size: B
• --eta: server step size
• --lr: local learning rate
• --rho: coefficient of the proximal parameter:
• --iid: 1 for IID, and 0 for non-IID
• --model: name of the model
• --dataset: name of the dataset

Publications

If you find this repository useful for your research, please cite the following paper:

@inproceedings{fedadmm,
  title={Fed{ADMM}: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity},
  author = {Gong, Yonghai and Li, Yichuan and Freris, Nikolaos M.},
  booktitle = {ICDE},
  pages = {2575-2587},
  year = {2022}
}

(If you have any questions, feel free to ask me via gongyh@mail.ustc.edu.cn)