一种基于带宽分配的联邦学习激励机制

doi:10.11959/j.issn.2096-3750.2022.00300

Abstract

Abstract:

Federated learning (FL) is an emerging machine learning paradigm that can make full use of crowd sourced mobile resources for training on decentralized data.However, it is challenging to deploy FL over a wireless network because of the limited bandwidth and clients’ selfishness.To address these challenges, an incentive mechanism with bandwidth allocation (IMBA) was proposed.Considering the difference between clients' data quality and computing power, IMBA designs a payment scheme to incentivize high-quality clients to contribute their computing resources, thus improving the training accuracy of the model.By minimizing the weight sum of training time and payment cost, the optimal payment and bandwidth allocation scheme was determined, and the training delay was reduced by optimizing bandwidth allocation.Experiments show that IMBA effectively improves training accuracy, reduces the training delay and helps the server flexibly balance training delay and hiring payment.

Key words: federated learning, incentive mechanism, bandwidth allocation, Stackelberg game, training quality

CLC Number:

TP393

Yingyun GUO, Bo GAO, Zhifei ZHANG, Yu ZHANG, Ke XIONG. An incentive mechanism with bandwidth allocation for federated learning[J]. Chinese Journal on Internet of Things, 2022, 6(4): 82-92.

Figures/Tables 10

符号	说明	符号	说明
$N / N$	用户数量/用户集合	$O_{n} / O_{n}$	用户n的训练数据数量/集合
B	总上行带宽	$λ / λ_{n}$	带宽分配比例向量/用户n的带宽比例
$g_{n}$	用户n与基站间信道增益	$h_{n}$	用户n的小尺度衰落分量
$r_{n}$	用户n的上行传输速率	$ρ_{n}$	用户n的阴影衰落模型
$ζ_{n}$	用户n的路径损失模型	$Z_{0}$	噪声功率
$p_{n}$	用户n的发射功率	D	训练模型大小
$t_{n}^{M}$	用户n的通信时间，M表示通信	$E_{n}^{M}$	用户n的通信功耗
$θ_{n}^{M}$	用户n的单位通信成本	$c_{n}^{M}$	用户n的通信成本
$I_{n}$	用户n的本地epoch数	$q_{n}$	用户n的数据质量
$Δ$	单位样本的大小	$e_{n}$	用户n计算芯片组的有效电容参数
$f_{n}$	用户n用于本地训练的CPU频率	$t_{n}^{P}$	用户n本地计算时间，P表示计算
$E_{n}^{P}$	用户n的本地计算功耗	$θ_{n}^{P}$	用户n单位计算成本
$c_{n}^{P}$	用户n的本地计算成本	$t_{n}$	用户n训练时间
$c_{n}$	用户n的本地成本	$δ_{n}$	用户n的数据非独立同分布程度
$μ_{n}$	用户n处理单位比特样本数据所需的CPU周期数	$τ / τ_{n}$	服务器的单位支付向量/用户n的单位报酬
$β$	时间与支付成本的权衡系数	$K / K$	参与训练用户数量/训练用户集合

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参数β/(×1 ¹⁰0 )	参与训练的用户序号
2，3，4	11，14，15，16，17，18，19，20
5，6	7，11，14，15，16，17，18，19，20
7，8，9，10，11，12	7，11，12，14，15，16，17，18，19，20
13	7，8，11，12，14，15，16，17，18，19，20
14	7，8，11，12，13，14，15，16，17，18，19，20
15	7，8，10，11，12，13，14，15，16，17，18，19，20

An incentive mechanism with bandwidth allocation for federated learning

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