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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (5): 892-899    DOI: 10.3785/j.issn.1008-973X.2023.05.005
计算机技术与控制工程     
基于无线D2D网络的分层联邦学习
刘翀赫(),余官定*(),刘胜利
浙江大学 信息与电子工程学院，浙江 杭州 310027
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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摘要：

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    
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
收稿日期: 2022-07-22 出版日期: 2023-05-09
CLC:  TN 929  
基金资助: 国家自然科学基金资助项目（61671407）
通讯作者: 余官定     E-mail: liuchonghe@zju.edu.cn;yuguanding@zju.edu.cn
作者简介: 刘翀赫（1999—） ，男，硕士生，从事联邦学习研究. orcid.org/0000-0002-1895-7076. E-mail: liuchonghe@zju.edu.cn
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引用本文:

刘翀赫,余官定,刘胜利. 基于无线D2D网络的分层联邦学习[J]. 浙江大学学报(工学版), 2023, 57(5): 892-899.

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.

链接本文:

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

图 1  基于无线D2D网络的分层联邦学习系统模型
图 2  分层联邦学习算法时序模型
图 3  在Cifar-10上训练CNN局部经验损失与度的关系
图 4  在MNIST上训练DNN局部经验损失与度的关系
图 5  不同算法下CNN在Cifar-10上的准确率随时间变化
图 6  不同算法下DNN在MNIST上的准确率随时间变化
图 7  不同簇头选择方法下CNN在Cifar-10上的准确率随时间变化
图 8  不同簇头选择方法下DNN在MNIST上的准确率随时间变化
图 9  不同全局聚合频率下CNN在Cifar-10上的准确率随时间变化
图 10  不同全局聚合频率下DNN在MNIST上的准确率随时间变化
1 CHEN M, YANG Z, SAAD W, et al A Joint learning and communications framework for federated learning over wireless networks[J]. IEEE Transactions on Wireless Communications, 2020, 20 (1): 269- 283
2 ZHU G, LIU D, DU Y, et al Towards an intelligent edge: wireless communication meets machine learning[J]. IEEE Communications Magazine, 2020, 58 (1): 19- 25
doi: 10.1109/MCOM.001.1900103
3 CHEN M, GUNDUZ D, HUANG K, et al Distributed learning in wireless networks: recent progress and future challenges[J]. IEEE Journal on Selected Areas in Communications, 2021, 39 (12): 3579- 3605
doi: 10.1109/JSAC.2021.3118346
4 MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data [C]// Artificial Intelligence and Statistics. Lauderdale: PMLR, 2017: 1273-1282.
5 LIM W Y B, LUONG N C, HOANG D T, et al Federated learning in mobile edge networks: a comprehensive survey[J]. IEEE Communications Surveys and Tutorials, 2020, 22 (3): 2031- 2063
doi: 10.1109/COMST.2020.2986024
6 LIM W Y B, NG J S, XIONG Z, et al Decentralized edge intelligence: a dynamic resource allocation framework for hierarchical federated learning[J]. IEEE Transactions on Parallel and Distributed Systems, 2021, 33 (3): 536- 550
7 WANG Z, XU H, LIU J, et al. Accelerating federated learning with cluster construction and hierarchical aggregation [EB/OL]. [2022-02-01]. https://www.computer.org/csdl/journal/tm/5555/01/09699080/1ADJeS80Gvm.
8 FENG D, LU L, YUANWU Y, et al Device-to-device communications in cellular networks[J]. IEEE Communications Magazine, 2014, 52 (4): 49- 55
doi: 10.1109/MCOM.2014.6807946
9 ASADI A, WANG Q, MANCUSO V A survey on device-to-device communication in cellular networks[J]. IEEE Communications Surveys and Tutorials, 2014, 16 (4): 1801- 1819
doi: 10.1109/COMST.2014.2319555
10 YU D, ZOU Z, CHEN S, et al Decentralized parallel sgd with privacy preservation in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70 (6): 5211- 5220
doi: 10.1109/TVT.2021.3064877
11 XING H, SIMEONE O, BI S. Decentralized federated learning via SGD over wireless D2D networks [C]// 21st International Workshop on Signal Processing Advances in Wireless Communications. Atlanta: IEEE, 2020: 1-5.
12 POKHREL S R, CHOI J. A decentralized federated learning approach for connected autonomous vehicles [C]// IEEE Wireless Communications and Networking Conference Workshops. Seoul : IEEE, 2020: 1-6.
13 ZHU L, LIU C, YUAN J, et al. Machine learning-based resource optimization for D2D communication underlaying networks [C]// IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). Victoria: IEEE, 2020: 1-6.
14 HOANG T D, LE L B, LENGOC T Resource allocation for D2D communication underlaid cellular networks using graph-based approach[J]. IEEE Transactions on Wireless Communications, 2016, 15 (10): 7099- 7113
doi: 10.1109/TWC.2016.2597283
15 OZFATURA E, RINI S, GÜNDÜZ D. Decentralized SGD with over-the-air computation [C]// IEEE Global Communications Conference. Taipei: IEEE, 2020: 1-6.
16 NISHIO T, YONETANI R. Client selection for federated learning with heterogeneous resources in mobile edge [C]// IEEE International Conference on Communications. Shanghai: IEEE, 2019: 1-7.
17 WEN D, BENNIS M, HUANG K Joint parameter-and-bandwidth allocation for improving the efficiency of partitioned edge learning[J]. IEEE Transactions on Wireless Communications, 2020, 19 (12): 8272- 8286
doi: 10.1109/TWC.2020.3021177
18 JIANG Z, YU G, CAI Y, Decentralized edge learning via unreliable device-to-device communications [J]. IEEE Transactions on Wireless Communications, 2022, 21(11): 9041-9055
19 WEN D, JEON K J, BENNIS M, et al Adaptive subcarrier, parameter, and power allocation for partitioned edge learning over broadband channels[J]. IEEE Transactions on Wireless Communications, 2021, 20 (12): 8348- 8361
doi: 10.1109/TWC.2021.3092075
20 ZENG Q, DU Y, HUANG K, et al Energy-efficient resource management for federated edge learning with CPU-GPU heterogeneous computing[J]. IEEE Transactions on Wireless Communications, 2021, 20 (12): 7947- 7962
doi: 10.1109/TWC.2021.3088910
21 KOLOSKOVA A, LOIZOU N, BOREIRI S, et al. A unified theory of decentralized sgd with changing topology and local updates [C]// International Conference on Machine Learning. Vienna: PMLR, 2020: 5381-5393.
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