A Hierarchical Federated Learning Framework Based on Shared Dataset and Gradient Compensation

doi:10.3969/j.issn.1671-1122.2023.12.002

Abstract

Abstract:

Federated learning(FL) enables vehicles to locally retain data for model training, enhancing privacy. However, due to variations in conditions such as onboard sensors and driving routes, vehicles participating in FL may exhibit different data distributions, thereby reducing model generalization and increasing convergence challenges. To ensure real-time performance, asynchronous stochastic gradient descent(SGD) techniques widely employes in Internet of vehicle. Nevertheless, the issue of gradient delay can lead to inaccuracies in model training. To address these challenges, this paper proposes a layered FL framework based on shared datasets and gradient compensation. The framework utilized shared datasets and an aggregation method weighted by ReLU values to reduce model bias. Additionally, it employed a Taylor expansion approximation of the original loss function using the gradient function to compensate for asynchronous SGD. Experimental results on the MNIST and CIFAR-10 datasets indicate that compared to FedAVG, MOON, and HierFAVG, the proposed method achieves an average accuracy improvement of 13.8%, 2.2%, and 3.5%, respectively. The time cost is only half that of both synchronous SGD and asynchronous SGD.

Key words: Internet of vehicle, federated learning, gradient compensation, heterogeneous data, asynchronous communication

CLC Number:

TP309

LIU Jiqiang, WANG Xuewei, LIANG Mengqing, WANG Jian. A Hierarchical Federated Learning Framework Based on Shared Dataset and Gradient Compensation[J]. Netinfo Security, 2023, 23(12): 10-20.

Figures/Tables 13

References 29

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符号表示	符号含义
${{\mathbf{\omega }}_{0}}$	全局初始梯度
Q	共享数据集
T	总聚合步骤
L	RSU总数量
${{C}_{l}}$	第l个RSU域内的车辆
${{P}_{i}}$	第i个车辆的数据集
${{k}_{1}}$	车辆本地训练迭代次数
${{k}_{2}}$	边缘聚合迭代次数
$\mathbf{\omega }_{i}^{l}$	使用数据集${{P}_{i}}$训练得到的局部模型
$\mathbf{\omega }_{e}^{l}$	第l个RSU利用Q训练得到的模型

实验环境	版本
CPU	Intel(R) Xeon(R) Gold 6330N CPU @2.20 GHz
GPU	NVIDIA GeForce RTX 4090
Python	3.7.6
PyTorch	1.0.0

Q	场景一		场景二
Q	准确率	损失	准确率	损失
0.1	82.97%	0.2980	81.02%	0.3011
0.2	91.64%	0.2931	92.11%	0.2760
0.5	90.76%	0.2951	90.10%	0.2802
1	89.09%	0.2937	91.24%	0.2795

	场景一
	MNIST（CNN模型）		CIFAR-10（CNN模型）
	准确率	损失	准确率	损失
本文算法	92.22%	0.2757	63.02%	0.2800
HierFAVG	89.73%	0.2976	60.19%	0.2876
MOON	90.03%	0.2991	59.45%	0.2911
FedAvg	78.41%	0.3166	55.43%	0.3601
	场景二
	MNIST（CNN模型）		CIFAR-10（CNN模型）
	准确率	损失	准确率	损失
本文算法	91.99%	0.2762	58.21%	0.3301
HierFAVG	88.49%	0.3253	52.11%	0.2991
MOON	88.01%	0.3213	52.66%	0.2989
FedAvg	81.44%	0.3701	43.02%	0.3729

算法	准确率	时间开销/s
本文算法	95.21%	3811.8
朴素异步通信算法	94.38%	7309.217
分层同步通信算法	91.94%	6491.8731