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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (5): 892-899    DOI: 10.3785/j.issn.1008-973X.2023.05.005
    
Hierarchical federated learning based on wireless D2D networks
Chong-he LIU(),Guan-ding YU*(),Sheng-li LIU
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
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Abstract  

A hierarchical federated learning framework based on wireless device-to-device (D2D) networks was proposed to solve the problem of large communication resource consumption and limited device computing resources faced by deploying federated learning in wireless networks. Different from the traditional architectures, the hierarchical aggregation was adopted for model training. The architecture performed the intra-cluster aggregation through D2D networks, and each cluster performed the decentralized training at the same time. A cluster head was selected from each cluster to upload the model to the server for global aggregation. The network traffic of the central node was reduced by combining the hierarchical federated learning and decentralized learning. The degree of the vertices in the D2D networks was used to measure the model convergence performance. The head selection and bandwidth allocation were jointly optimized by maximizing the total degree of selected cluster heads. An optimization algorithm based on dynamic programming was designed to obtain the optimal solutions. The simulation results show that compared with the baseline algorithm,the framework can not only effectively reduce the frequency of global aggregation and training time, but also improve the performance of the final model.

Key words： federated learning      device-to-device communication      decentralized learning      resource allocation      training acceleration     
Received: 22 July 2022      Published: 09 May 2023
CLC:  TN 929  
Fund:  国家自然科学基金资助项目（61671407）
Corresponding Authors: Guan-ding YU     E-mail: liuchonghe@zju.edu.cn;yuguanding@zju.edu.cn
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Chong-he LIU
Guan-ding YU
Sheng-li LIU
Cite this article:

Chong-he LIU,Guan-ding YU,Sheng-li LIU. Hierarchical federated learning based on wireless D2D networks. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 892-899.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.05.005     OR     https://www.zjujournals.com/eng/Y2023/V57/I5/892


基于无线D2D网络的分层联邦学习

为了解决在无线网络中部署联邦学习面临的通信资源消耗大和设备计算资源有限的问题，提出一种基于无线设备直通(D2D)网络的分层联邦学习框架. 与传统架构不同，模型训练采用分层聚合. 该框架通过D2D网络进行簇内聚合，各个簇同时进行去中心化训练,从每个簇中选择一个簇头上传模型至服务器进行全局聚合. 通过将去中心化学习与分层联邦学习结合，降低了中央节点网络流量. 使用D2D网络中节点的度来衡量模型收敛性能，通过最大化所有簇头的度之和，对簇头选择与带宽分配问题进行联合优化，并且设计一种基于动态规划的算法求出最优解. 仿真结果表明，与基线算法相比，该框架不仅能够有效地降低全局聚合的频率和减少训练时间，而且能够提高最终训练得到的模型性能.


关键词： 联邦学习,  设备直通网络,  去中心化学习,  资源分配,  训练加速 
Fig.1 System model of hierarchical federated learning system based on wireless D2D networks
Fig.2 Sequence model of hierarchical federated learning
Fig.3 Local experience loss versus degree for training CNN on Cifar-10
Fig.4 Local experience loss versus degree for training DNN on MNIST
Fig.5 Accuracy of CNN on Cifar-10 varies with time in different algorithms
Fig.6 Accuracy of DNN on MNIST varies with time in different algorithms
Fig.7 Accuracy of CNN on Cifar-10 varies with time in different cluster head selection methods
Fig.8 Accuracy of DNN on MNIST varies with time in different cluster head selection methods
Fig.9 Accuracy of CNN on Cifar-10 varies with time in different global aggregation frequencies
Fig.10 Accuracy of DNN on MNIST varies with time in different global aggregation frequencies
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