基于激励机制的联邦学习优化算法

doi:10.11959/j.issn.1000-436x.2023095

Abstract

Abstract:

Federated learning optimization algorithm based on incentive mechanism was proposed to address the issues of multiple iterations, long training time and low efficiency in the training process of federated learning.Firstly, the reputation value related to time and model loss was designed.Based on the reputation value, an incentive mechanism was designed to encourage clients with high-quality data to join the training.Secondly, the auction mechanism was designed based on the auction theory.By auctioning local training tasks to the fog node, the client entrusted the high-performance fog node to train local data, so as to improve the efficiency of local training and solve the problem of performance imbalance between clients.Finally, the global gradient aggregation strategy was designed to increase the weight of high-precision local gradient in the global gradient and eliminate malicious clients, so as to reduce the number of model training.

Key words: federated learning, incentive mechanism, reputation value, auction strategy, aggregation strategy

CLC Number:

TN92

Youliang TIAN, Shihong WU, Ta LI, Lindong WANG, Hua ZHOU. Federated learning optimization algorithm based on incentive mechanism[J]. Journal on Communications, 2023, 44(5): 169-180.

Figures/Tables 14

References 34

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参数	描述
data_size	本地训练数据的大小
P_i	雾节点i竞拍的价格
T_i	雾节点i一次迭代的时间
c_n	执行一个数据样本的CPU周期数
f_i	设备i的频率
	客户端i用于计算的能量消耗
	客户端i在第n轮训练后的模型损失
	第n轮训练后的平均模型损失
d	任务发布者与客户端签署合约需要交纳的押金
r	客户端与雾节点签署合约需要交纳的押金
task_x	任务x
curret_task	当前任务
	客户端i的第 j轮训练梯度
w^j	第 j轮训练的全局梯度
T_ex	任务发布者期望的总训练时间
T_r	实际训练的总时间
t ^ji	客户端i在第 j轮训练所用的时间

方案	MNIST	CIFAR-10
本文方案	96.3%	96.1%
FAIR方案	51.8%	51.8%

方案	MNIST/s	CIFAR-10/s
本文方案	421.4	391.58
FAIR方案	611.4	568.85

方案	MNIST/s	CIFAR-10/s
本文方案	296.03+	1 579.82+
FedAvg方案	550.37	2 682.26
FAIR方案	467.92	2 490.69

方案	客户端激励	流浪者效应	聚合策略
FedPA方案	×	√	×
FedAvg方案	×	×	×
FAIR方案	√	×	√
本文方案	√	√	√

Federated learning optimization algorithm based on incentive mechanism

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Figures/Tables 14

References 34

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方案	MNIST（96%）/轮	CIFAR-10（60%）/轮
本文方案	16	86
FedAvg方案	20	98
FAIR方案	17	91

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