This repository contains the code accompanying the AAMAS2023 paper " FedMM: A Communication Efficient Solver for Federated Adversarial Domain Adaptation" Paperlink:
- Python 3.7
- Tensorflow 1.14.0
- numpy 1.20.3
- tqdm
Download mnistm data:
curl -L -O http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz
Preprocess mnistm dataset
python create_mnistm.py
Usage for the Proposed FedMM on DANN loss:
python train.py -max_iter=15000 -lambda1_decay=1.05 -adv_loss='DANN'
Usage for the Proposed FedMM on MDD loss:
python train.py -max_iter=50000 -lambda1_decay=1.01 -adv_loss='MDD'
Usage for the Proposed FedMM on CDAN loss
python train.py -max_iter=30000 -lambda1_decay=1.02 -adv_loss='CDAN'
@inproceedings{shen2023fedmm,
title={FedMM: A Communication Efficient Solver for Federated Adversarial Domain Adaptation},
author={Shen, Yan and Du, Jian and Zhao, Han and Ji, Zhanghexuan and Ma, Chunwei and Gao, Mingchen},
booktitle={Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages={1808--1816},
year={2023}
}