基于标签量信息的联邦学习节点选择算法

doi:10.11959/j.issn.2096-3750.2021.00249

Chinese Journal on Internet of Things ›› 2021, Vol. 5 ›› Issue (4): 46-53.doi: 10.11959/j.issn.2096-3750.2021.00249

Special Issue: 联邦学习

• Theory and Technology • Previous Articles Next Articles

Node selection based on label quantity information in federated learning

Jiahua MA¹, Xinghua SUN¹, Wenchao XIA², Xijun WANG¹, Hongzhou TAN¹, Hongbo ZHU²

¹ Sun Yat-sen University, Guangzhou 510006, China
² Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Revised:2021-11-23 Online:2021-12-30 Published:2021-12-01
Supported by:
The National Key Research and Development Program of China(2019YFE0114000);The National Natural Science Foundation of China(92067201);The Natural Science Foundation of Jiangsu Province(BK20212001);The Guang-dong Basic and Applied Basic Research Foundation(2021A1515012631);The Guang-dong Basic and Applied Basic Research Foundation(2019A1515011906)

Abstract

Abstract:

Aiming at the problem that the difference of node data distribution has adverse effect on the performance of federated learning algorithm, a node selection algorithm based on label quantity information was proposed.An optimization objective based on the label quantity information of nodes was designed, considering the optimization problem of selecting the nodes with balanced label distribution under a certain time consumption limit.According to the correlation between the aggregated label distribution of selected nodes and the convergence of the global model, the upper bound of the weight divergence of the global model was reduced to improve the convergence stability of the algorithm.Simulation results shows that the new algorithm had higher convergence efficiency than the existing node selection algorithm.

Key words: federated learning, node selection, communication delay

CLC Number:

TP391.4

Jiahua MA, Xinghua SUN, Wenchao XIA, Xijun WANG, Hongzhou TAN, Hongbo ZHU. Node selection based on label quantity information in federated learning[J]. Chinese Journal on Internet of Things, 2021, 5(4): 46-53.

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References 16

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算法名	运行回合总数			到达目标识别率耗时
算法名	运行回合总数	50%	60%	70%	80%	85%
FedBag	1 169	3.924 2 h	6.111 9 h	17.152 5 h	31.633 3 h	59.159 9 h
FedCS	1 235	5.038 0 h	9.479 3 h	20.023 8 h	44.540 4 h	78.161 5 h
RS	364	11.506 3 h	19.463 7 h	45.226 8 h	111.395 5 h	207.675 1 h
EM	1 398	3.881 7 h	7.988 1 h	13.445 8 h	42.140 8 h	66.910 1 h

T_limit/s	运行回合总数			到达目标识别率耗时
T_limit/s	运行回合总数	50%	60%	70%	80%	85%
100	2320	2.9230 h	4.3990 h	9.8239 h	21.2913 h	28.4150 h
200	1169	3.9242 h	6.1119 h	17.1525 h	31.6333 h	59.1599 h
300	872	3.3955 h	9.0295 h	16.4829 h	43.4716 h	78.7371 h

Node selection based on label quantity information in federated learning

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