This repository contains the code accompanying the AAMAS2023 paper " FedMM: A Communication Efficient Solver for Federated Adversarial Domain Adaptation" Paperlink:

Requirements to run the code:

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1. Python 3.7
2. Tensorflow 1.14.0
3. numpy 1.20.3
4. tqdm

Download dataset:

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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 

Experiments on Federated Domain Adaptation:

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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'

Reference

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@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}
}