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FCCL

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TPAMI2023 & CVPR2022 - Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning & Learn From Others and Be Yourself in Heterogeneous Federated Learning

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최종 버전 다운로드 (.zip)

- - init_dataset.py
  - utils_dataset.py
- - _base.py
  - evaluation_generalization.py
  - params.py
  - utils_idea.py
- - efficientnet.py
  - googlenet.py
  - mobilnet_v2.py
  - resnet.py
  - shufflenet.py
  - train_network.py
  - utils_network.py
- - efficientnet.py
  - googlenet.py
  - mobilnet_v2.py
  - ResNet.py
  - shufflenet.py
- - __init__.py
  - cifar100.py
  - digits.py
  - fashion_mnist.py
  - market1501.py
  - office31.py
  - officecaltech.py
  - officehome.py
  - tinyimagenet.py
- - __init__.py
  - fccl.py
  - fcclplus.py
  - fedavg.py
  - feddf.py
  - fedprox.py
  - moon.py
- - __init__.py
  - args.py
  - best_args.py
  - conf.py
  - logger.py
  - supcon.py
  - training.py
  - util.py
- analysis.py
- main.py
- run.sh
- run_new.sh
.gitignore
README.md

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# Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
> Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning          
> Wenke Huang, Mang Ye, Zekun Shi, Bo Du
> *TPAMI, 2023*

## Abstract
Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data.  Model heterogeneity and  catastrophic forgetting are two crucial challenges, which greatly limit the applicability and generalizability. This paper presents a novel FCCL+, federated correlation and similarity learning with non-target distillation, facilitating the both intra-domain discriminability and inter-domain generalization. For heterogeneity issue, we leverage irrelevant unlabeled public data for communication between the heterogeneous participants. We construct cross-correlation matrix and align instance similarity distribution on both logits and feature levels, which effectively overcomes the communication barrier and improves the generalizable ability. For catastrophic forgetting in local updating stage, FCCL+ introduces Federated Non Target Distillation, which retains inter-domain knowledge while avoiding the optimization conflict issue, fulling distilling privileged inter-domain information through depicting posterior classes relation. Considering that there is no standard benchmark for evaluating existing heterogeneous federated learning under the same setting, we present a comprehensive benchmark with extensive representative methods under four domain shift scenarios, supporting both heterogeneous and homogeneous federated settings. Empirical results demonstrate the superiority of our method and the efficiency of modules on various scenarios.

# Learn from others and Be yourself in Heterogeneous Federated Learning
> Learn from others and Be yourself in Heterogeneous Federated Learning            
> Wenke Huang, Mang Ye, Bo Du
> *CVPR, 2022*
> [Link](https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Learn_From_Others_and_Be_Yourself_in_Heterogeneous_Federated_Learning_CVPR_2022_paper.pdf)

## Abstract
Federated learning has emerged as an important distributed learning paradigm, which normally involves collaborative updating with others and local updating on private data. However, heterogeneity problem and catastrophic forgetting bring distinctive challenges. First, due to non-i.i.d (identically and independently distributed) data and heterogeneous architectures, models suffer performance degradation on other domains and communication barrier with participants models. Second, in local updating, model is separately optimized on private data, which is prone to overfit current data distribution and forgets previously acquired knowledge, resulting in catastrophic forgetting. In this work, we propose FCCL (Federated Cross-Correlation and Continual Learning). For heterogeneity problem, FCCL leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift. Meanwhile, for catastrophic forgetting, FCCL utilizes knowledge distillation in local updating, providing inter and intra domain information without leaking privacy. Empirical results on various image classification tasks demonstrate the effectiveness of our method and the efficiency of modules.

## Citation
```
@inproceedings{FCCL_CVPR22,
    title={Learn from others and be yourself in heterogeneous federated learning},
    author={Huang, Wenke and Ye, Mang and Du, Bo},
    booktitle={CVPR},
    year={2022}
}
@article{FCCLPlus_TPAMI23,
  title={Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning}, 
  author={Wenke Huang and Mang Ye and Zekun Shi and Bo Du},
  year={2023},
  journal={TPAMI}
}
```

## Relevant Projects
[1] Rethinking Federated Learning with Domain Shift: A Prototype View - CVPR 2023 [[Link](https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.pdf)] [[Code](https://github.com/WenkeHuang/RethinkFL)]

[2] Federated Graph Semantic and Structural Learning - IJCAI 2023 [[Link](https://marswhu.github.io/publications/files/FGSSL.pdf)][[Code](https://github.com/wgc-research/fgssl)]