FederatedRSCD

Remote Sensing Change Detection with Federated Learning.

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Introduction

A federated learning framework for remote sensing change detection, supporting multiple datasets, model architectures, and loss functions. The framework implements FedAvg for weight aggregation and provides a realistic Non-IID benchmark with data volume imbalance and sampler heterogeneity across clients.

Requirements

• Python 3.10+
• PyTorch >= 2.0

pip install -r requirements.txt

Supported Models

13 model architectures are available, selectable via --model_name:

Model	Architecture	Parameters	Reference
BASE_Transformer	BIT (ResNet18 + Transformer)	11.9M	Chen et al., "Remote Sensing Image Change Detection With Transformers"
BASE_Transformer_s4_dd8	BIT + decoder depth 8	12.4M	-
BASE_Transformer_s4_dd8_dedim8	BIT + decoder dim 8	11.9M	-
SiamUnet_diff	Siamese U-Net (difference)	1.4M	Daudt et al., "Fully Convolutional Siamese Networks for Change Detection"
SiamUnet_conc	Siamese U-Net (concatenation)	1.5M	Daudt et al.
Unet	FC-EF (early fusion)	1.4M	Daudt et al.
ChangeFormerV1	Transformer encoder + conv decoder	38.2M	Bandara et al., "ChangeFormer: A Transformer-Based Siamese Network"
ChangeFormerV2	+ Transformer decoder	32.7M	-
ChangeFormerV3	+ Feature fuse	32.7M	-
ChangeFormerV4	+ Dual-branch encoder	35.8M	-
ChangeFormerV5	+ Deep encoder	55.3M	-
ChangeFormerV6	+ Lightweight encoder	41.0M	-
DTCDSCN	Dual-task constrained SCN	41.1M	"Building Change Detection Using a Dual Task Constrained Deep Siamese Convolutional Network"

Supported Loss Functions

4 loss functions are available, selectable via --loss_type:

Loss	Description
ce	Cross Entropy (default)
focal	Focal Loss - addresses class imbalance
dice	Dice Loss - optimized for segmentation
ce_dice	CE + Dice combination (0.5 weight each)

Datasets

Pre-cropped datasets are organized as follows:

datasets/
├── LEVIR/          # LEVIR-CD
├── S2Looking/      # S2Looking
└── WHUCD/          # WHU-CD

Client Partitioning

Summary
├── Total Datasets: 3
├── Total Clients: 8
└── Total Training Samples: 26,632

LEVIR (2 clients)
├── Client 1: 2,563 samples (60%), Random sampler
└── Client 2: 1,139 samples (40%), Weighted sampler

S2Looking (4 clients)
├── Client 3: 14,000 samples (50%), Random sampler
├── Client 4:  5,040 samples (30%), Sequential sampler
├── Client 5:  1,260 samples (15%), Random sampler
└── Client 6:    140 samples  (5%), Weighted sampler

WHUCD (2 clients)
├── Client 7: 1,245 samples (50%), Random sampler
└── Client 8: 1,245 samples (50%), Sequential sampler

This partitioning introduces both data volume imbalance and sampler heterogeneity, forming a realistic Non-IID federated learning benchmark.

Data Preprocessing

Crop raw datasets to 256x256 patches:

# See data/crop_dataset.py for details
python -c "from data.crop_dataset import crop_levir, crop_s2looking, crop_whu; ..."

Usage

Quick Start

# Default: BASE_Transformer + CE loss
python main.py --datasets /path/to/datasets/

Select Model

# Siamese U-Net (difference)
python main.py --model_name SiamUnet_diff

# ChangeFormer V6 with custom embed dimension
python main.py --model_name ChangeFormerV6 --embed_dim 256

# DTCDSCN
python main.py --model_name DTCDSCN

Select Loss Function

# Focal loss (for imbalanced data)
python main.py --loss_type focal

# Dice loss (for segmentation)
python main.py --loss_type dice

# Combined CE + Dice
python main.py --loss_type ce_dice

Full Arguments

Model Selection:
  --model_name {BASE_Transformer,BASE_Transformer_s4_dd8,BASE_Transformer_s4_dd8_dedim8,
                SiamUnet_diff,SiamUnet_conc,Unet,
                ChangeFormerV1,ChangeFormerV2,ChangeFormerV3,ChangeFormerV4,
                ChangeFormerV5,ChangeFormerV6,DTCDSCN}
                        Model architecture (default: BASE_Transformer)
  --embed_dim EMBED_DIM
                        Embedding dimension for ChangeFormerV5/V6 (default: 256)

Loss Function:
  --loss_type {ce,focal,dice,ce_dice}
                        Loss function type (default: ce)

Training:
  --batch_size BATCH_SIZE
                        Batch size (default: 64)
  --num_epochs NUM_EPOCHS
                        Number of federated rounds (default: 10)
  --num_client_epoch NUM_CLIENT_EPOCH
                        Local epochs per client per round (default: 2)
  --lr LR               Learning rate (default: 0.0001)
  --weight_decay WEIGHT_DECAY
                        Weight decay (default: 0.0001)

Federated Learning:
  --frac FRAC           Fraction of clients per round (default: 0.5)
  --niid / --no-niid    Non-IID data distribution (default: --niid)
  --n_shards N_SHARDS   Number of shards for partitioning (default: 20)

Data:
  --datasets DATASETS   Root directory of datasets
  --num_workers_dataloader NUM_WORKERS
                        Data loading workers per client (default: 4)

Other:
  --device DEVICE       Device (default: cuda:0)
  --eval_interval EVAL_INTERVAL
                        Evaluation interval in rounds (default: 1)
  --save_dir SAVE_DIR   Directory to save models (default: ./saved_models)

Example Commands

# Lightweight model + focal loss for imbalanced data
python main.py --model_name SiamUnet_diff --loss_type focal --datasets /path/to/data

# ChangeFormerV5 with dice loss
python main.py --model_name ChangeFormerV5 --embed_dim 256 --loss_type dice

# DTCDSCN with combined loss, more rounds, higher participation
python main.py --model_name DTCDSCN --loss_type ce_dice --num_epochs 50 --frac 0.8

# BIT with deeper decoder
python main.py --model_name BASE_Transformer_s4_dd8 --loss_type ce --lr 0.0005

Project Structure

FederatedRSCD/
├── main.py                     # Entry point, FedTrain class
├── loss.py                     # Loss functions (CE, Focal, Dice, CE+Dice)
├── assgin_ds.py                # Dataset loading entry
├── requirements.txt
├── configs/
│   ├── __init__.py
│   ├── dataset_config.py       # Dataset & client partition configs
│   └── model_config.py         # Model configs + factory function
├── backbone/
│   ├── BaseTransformer.py      # BIT / BASE_Transformer + ResNet
│   ├── ChangeFormer.py         # ChangeFormer V1-V6
│   ├── ChangeFormerBaseNetworks.py
│   ├── DTCDSCN.py              # CDNet34
│   ├── SiamUnet_diff.py        # Siamese U-Net (difference)
│   ├── SiamUnet_conc.py        # Siamese U-Net (concatenation)
│   ├── Unet.py                 # FC-EF
│   ├── resnet.py               # Custom ResNet backbone
│   └── help_funcs.py           # Transformer, attention modules
├── data/
│   ├── crop_dataset.py         # Dataset preprocessing
│   ├── data_augment.py         # Data augmentation
│   ├── fed_allocator.py        # Federated data allocation
│   ├── fed_sampler.py          # Sampler factory
│   ├── collate_func.py         # Collate function
│   └── data_processing.py      # Dataset classes
├── utils/
│   ├── args.py                 # CLI argument parser
│   └── tools.py                # Metrics, display utilities
└── elements/
    └── image.png               # Architecture diagram

Citation

@article{zhao2025fedrs,
  title={FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing},
  author={Zhao, Haodong and Peng, Peng and Chen, Chiyu and Huang, Linqing and Liu, Gongshen},
  journal={arXiv preprint arXiv:2505.08325},
  year={2025}
}

AI-assisted Development

This project was developed with the assistance of AI tools (GLM) for:

• Code structuring and refactoring
• Documentation drafting and polishing
• Debugging and design discussions

All model design, experiments, and final decisions were made by the author.