Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (12): 3759-3765.DOI: 10.11772/j.issn.1001-9081.2023121740
• Artificial intelligence • Previous Articles Next Articles
Zucuan ZHANG1,2,3, Xuebin CHEN1,2,3(), Rui GAO1,2,3, Yuanhuai ZOU1,2,3
Received:
2023-12-18
Revised:
2024-04-13
Accepted:
2024-04-17
Online:
2024-05-07
Published:
2024-12-10
Contact:
Xuebin CHEN
About author:
ZHANG Zucuan, born in 1998, M. S. candidate. His research interests include data security, federated learning.Supported by:
张祖篡1,2,3, 陈学斌1,2,3(), 高瑞1,2,3, 邹元怀1,2,3
通讯作者:
陈学斌
作者简介:
张祖篡(1998—),男,江苏徐州人,硕士研究生,CCF会员,主要研究方向:数据安全、联邦学习基金资助:
CLC Number:
Zucuan ZHANG, Xuebin CHEN, Rui GAO, Yuanhuai ZOU. Federated learning client selection method based on label classification[J]. Journal of Computer Applications, 2024, 44(12): 3759-3765.
张祖篡, 陈学斌, 高瑞, 邹元怀. 基于标签分类的联邦学习客户端选择方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3759-3765.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023121740
方法 | MNIST | Fashion-MNIST | Cifar-10 |
---|---|---|---|
FedAvg | 86.347 500 09 | 62.822 813 03 | 28.779 374 96 |
FedProx | 89.433 438 31 | 65.895 312 19 | 40.324 999 93 |
FedLCCS+FedAvg | 95.477 172 61 | 79.727 617 09 | 44.428 515 32 |
FedLCCS+FedProx | 95.965 331 79 | 81.777 076 48 | 48.135 447 50 |
Tab. 1 Comparison of accuracy of different methods
方法 | MNIST | Fashion-MNIST | Cifar-10 |
---|---|---|---|
FedAvg | 86.347 500 09 | 62.822 813 03 | 28.779 374 96 |
FedProx | 89.433 438 31 | 65.895 312 19 | 40.324 999 93 |
FedLCCS+FedAvg | 95.477 172 61 | 79.727 617 09 | 44.428 515 32 |
FedLCCS+FedProx | 95.965 331 79 | 81.777 076 48 | 48.135 447 50 |
方法 | 第1次达到阈值的轮次 | 第10次达到阈值的轮次 | ||||
---|---|---|---|---|---|---|
MNIST | Fashion- MNIST | Cifar- 10 | MNIST | Fashion- MNIST | Cifar- 10 | |
FedAvg | 39 | 21 | 21 | 53 | 46 | 54 |
FedProx | 23 | 17 | 13 | 39 | 43 | 27 |
FedLCCS+FedAvg | 8 | 9 | 11 | 22 | 18 | 23 |
FedLCCS+FedProx | 8 | 6 | 10 | 19 | 17 | 22 |
Tab. 2 Comparison of convergence speed of different methods
方法 | 第1次达到阈值的轮次 | 第10次达到阈值的轮次 | ||||
---|---|---|---|---|---|---|
MNIST | Fashion- MNIST | Cifar- 10 | MNIST | Fashion- MNIST | Cifar- 10 | |
FedAvg | 39 | 21 | 21 | 53 | 46 | 54 |
FedProx | 23 | 17 | 13 | 39 | 43 | 27 |
FedLCCS+FedAvg | 8 | 9 | 11 | 22 | 18 | 23 |
FedLCCS+FedProx | 8 | 6 | 10 | 19 | 17 | 22 |
方法 | MNIST | Fashion-MNIST | Cifar-10 |
---|---|---|---|
FedAvg | 209.197 206 73 | 204.497 248 88 | 216.265 940 18 |
FedProx | 263.390 083 55 | 259.871 740 10 | 296.034 562 34 |
FedLCCS+FedAvg | 176.869 442 22 | 132.991 822 95 | 199.825 600 86 |
FedLCCS+FedProx | 216.986 409 66 | 212.674 599 40 | 231.530 412 67 |
Tab. 3 Comparison of running time of different methods
方法 | MNIST | Fashion-MNIST | Cifar-10 |
---|---|---|---|
FedAvg | 209.197 206 73 | 204.497 248 88 | 216.265 940 18 |
FedProx | 263.390 083 55 | 259.871 740 10 | 296.034 562 34 |
FedLCCS+FedAvg | 176.869 442 22 | 132.991 822 95 | 199.825 600 86 |
FedLCCS+FedProx | 216.986 409 66 | 212.674 599 40 | 231.530 412 67 |
方法 | m | 准确率/% | 方法 | m | 准确率/% |
---|---|---|---|---|---|
FedAvg | 5 | 91.077 500 34 | FedLCCS+FedAvg | 5 | 95.636 867 05 |
10 | 90.654 687 41 | 10 | 95.505 434 32 | ||
20 | 86.347 500 09 | 20 | 95.477 172 61 | ||
FedProx | 5 | 93.635 625 12 | FedLCCS+FedProx | 5 | 96.686 664 82 |
10 | 90.929 062 84 | 10 | 96.655 052 19 | ||
20 | 89.433 438 31 | 20 | 95.965 331 79 |
Tab. 4 Comparison of method accuracy under different slice numbers
方法 | m | 准确率/% | 方法 | m | 准确率/% |
---|---|---|---|---|---|
FedAvg | 5 | 91.077 500 34 | FedLCCS+FedAvg | 5 | 95.636 867 05 |
10 | 90.654 687 41 | 10 | 95.505 434 32 | ||
20 | 86.347 500 09 | 20 | 95.477 172 61 | ||
FedProx | 5 | 93.635 625 12 | FedLCCS+FedProx | 5 | 96.686 664 82 |
10 | 90.929 062 84 | 10 | 96.655 052 19 | ||
20 | 89.433 438 31 | 20 | 95.965 331 79 |
方法 | 准确率/% | 方法 | 准确率/% | ||
---|---|---|---|---|---|
FedAvg | 0.9 | 86.347 500 09 | FedLCCS+FedAvg | 0.9 | 90.962 725 16 |
0.6 | 86.347 500 09 | 0.6 | 94.317 240 24 | ||
0.3 | 86.347 500 09 | 0.3 | 97.744 347 12 | ||
FedProx | 0.9 | 89.433 438 31 | FedLCCS+FedProx | 0.9 | 91.759 270 67 |
0.6 | 89.433 438 31 | 0.6 | 95.490 865 23 | ||
0.3 | 89.433 438 31 | 0.3 | 98.487 850 91 |
Tab. 5 Comparison of methods accuracy under different target label array selection ratios
方法 | 准确率/% | 方法 | 准确率/% | ||
---|---|---|---|---|---|
FedAvg | 0.9 | 86.347 500 09 | FedLCCS+FedAvg | 0.9 | 90.962 725 16 |
0.6 | 86.347 500 09 | 0.6 | 94.317 240 24 | ||
0.3 | 86.347 500 09 | 0.3 | 97.744 347 12 | ||
FedProx | 0.9 | 89.433 438 31 | FedLCCS+FedProx | 0.9 | 91.759 270 67 |
0.6 | 89.433 438 31 | 0.6 | 95.490 865 23 | ||
0.3 | 89.433 438 31 | 0.3 | 98.487 850 91 |
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