Federated Representation Optimal Matching for Robust Federated Learning with Noisy Labels in Remote Sensing

This repository contains the implementation of our paper "Representation Optimal Matching for Robust Federated Learning with Noisy Labels in Remote Sensing". We introduce FedROM, a novel federated learning framework that addresses label noise in distributed remote sensing tasks without requiring auxiliary data or transmitting extra sensitive information. The full paper will be coming soon.

• Python 3.8+
• PyTorch 1.11.0

pip install -r requirements.txt

Please download the dataset from this link. The dataset file structure should be:

./dataset
├── FedRS
│   ├── NIID-1
│   ├── NIID-2
│   ├── val_balanced
│   └── val_imbalanced
├── FedRS-5
│   ├── NIID-1
│   ├── NIID-2
│   ├── val_balanced
│   └── val_imbalanced
└── FedRS.7z (extract this .7z file to get all datasets)

python FL_train.py --alg fedrom --model resnet18 --dataset RS-5 --noise_rate 0.8 --noise_pattern symmetric

• --alg: Federated learning algorithm (fedrom, fedavg, fedprox, etc.)
• --model: Model architecture (resnet18, alexnet, etc.)
• --dataset: Dataset (RS-5, RS-15)
• --noise_rate: Label noise rate (0.0-1.0)
• --noise_pattern: Noise pattern (symmetric, asymmetric)

For more configuration options, see args.py.

ROT/
├── algorithms/          # Federated learning algorithms
│   ├── fedrom.py       # FedROM core algorithm
│   ├── fedavg.py       # FedAvg algorithm
│   └── ...
├── utils/              # Utility functions
│   ├── model.py        # Model definitions
│   ├── datasets.py     # Dataset processing
│   └── utils.py        # General utilities
├── FL_train.py         # Main training script
├── args.py             # Parameter configuration
└── requirements.txt    # Dependencies

FedROM demonstrates robust performance across 18 baseline methods on two real-world remote sensing datasets with varying label noise rates and multi-source domains.

If you use this code in your research, please cite our paper:

@article{fedrom,
  author    = {Xuefeng Jiang and
               Tian Wen and
               Sheng Sun and
               Jinliang Yuan and
               Huashuo Liu and
               Peng Li and
               Lihua Wu and
               Yuwei Wang and
               Min Liu},
  title     = {Representation Optimal Matching for Federated Learning with Noisy Labels
               in Remote Sensing},
  journal   = {{IEEE} Transactions on Mobile Computing},
  year      = {2025},
  doi       = {10.1109/TMC.2025.3649179}
}

This project is licensed under the MIT License - see the LICENSE file for details.

We sincerely appreciate the authors of the FedRS datasets. More information can be found at FedRS.