DASFAA2023 - FedGR :Federated Learning with Gravitation Regulation for Double Imbalance Distribution
Federated learning for double unbalance settings (sample quantities imbalance for different classes in client and label or class imbalance for different client cross-client)
Algorithms | CIFAR-10 (2) | CIFAR-10 (3) | CIFAR-100 (20) | CIFAR-100 (30) |
---|---|---|---|---|
Acc(%) | Acc(%) | Acc(%) | Acc(%) | |
FedAvg | 50.36 | 53.76 | 36.15 | 42.19 |
FedProx | 48.84 | 54.94 | 36.24 | 42.21 |
FedNova | 56.33 | 68.63 | 38.63 | 45.35 |
SCAFFOLD | 57.37 | 67.32 | 38.43 | 46.82 |
PerFedAvg | 44.67 | 54.87 | 35.98 | 40.14 |
pFedMe | 45.81 | 50.18 | 35.36 | 40.18 |
FedOpt | 62.37 | 70.63 | 42.37 | 49.63 |
MOON | 61.45 | 70.45 | 40.53 | 47.91 |
FedRS | 63.22 | 73.56 | 42.76 | 50.73 |
FedGC | 62.91 | 72.11 | 42.11 | 50.21 |
FedGR(ours) | 67.84 | 77.86 | 45.44 | 53.16 |
python main_fed.py -algo fedgr/fednova/fedavg/fedopt/moon -dataset cifar10/cifar100/fashion-mnist
This is the code for the 2023 DASFAA paper: FedGR: Federated Learning with Gravitation Regulation for Double Imbalance Distribution. Please cite our paper if you use the code:
@inproceedings{Guo2023FedGR
author = {Songyue Guo and
Xu Yang and
Jiyuan Feng and
Ye Ding and
Wei Wang and
Yunqing Feng and
Qing Liao},
title = {FedGR: Federated Learning with Gravitation Regulation for Double Imbalance Distribution
},
booktitle = {Database Systems for Advanced Applications - 28th International Conference,
{DASFAA} 2023, Tianjin, China, April 17-20, 2023},
publisher = {Springer},
year = {2023}
}