6G联邦边缘学习新范式：基于任务导向的资源管理策略

doi:10.11959/j.issn.1000-436x.2022128

Abstract

Abstract:

Objectives: To make full use of the abundant data distributed at the edge of the network to serve the training of artificial intelligence models, edge intelligence technology represented by federated edge learning emerges as the times require. The rich data at the edge enables it to serve artificial intelligence model training, and design wireless resource management strategies with the goal of optimizing learning performance (such as optimizing model training time, learning convergence, etc.).

Methods: Using the federated learning network architecture, this paper analyzes the resource allocation and user scheduling schemes: 1. For the resource allocation problem, the trade-off relationship between the number of communication rounds and the delay per round when the goal of minimizing the total training time is analyzed, To meet the time constraints of each round of training, more frequency bandwidth should be allocated to devices with low computational power, compensating for a computational time by reducing communication time, and vice versa. Therefore, bandwidth allocation between devices should consider both channel conditions and computing resources, which is completely different from the traditional bandwidth allocation work that only considers channel conditions. To this end, it is necessary to model the total training time minimization problem, optimize the quantization series and bandwidth allocation, and design an alternate optimization algorithm to solve this problem. 2. For the user scheduling problem, the communication time minimization optimization problem is modeled by linking the importance of data with the number of communication rounds, channel quality and single-round communication delay, and using a theoretical model to unify the two. By solving the optimization problem, it is found that the optimal scheduling strategy will pay more attention to the importance of data in the early stage, and pay more attention to the channel quality in the later stage. In addition, the proposed single-device scheduling algorithm is also extended to multi-device scheduling scenarios.

Results: 1. For the resource allocation problem, when the bandwidth allocation is optimal, the relationship between the total training time and the quantization level obtained by simulation, run the same training process at least 5 times on each quantization level, and there is a total training time. The total training time is $T = N_{ϵ} \cdot T_{d}$ , and $N_{ϵ}$ is a decreasing function of quantization level q,and T_d is an increasing function of q . In addition, the optimal quantization series obtained through theoretical optimization is consistent with the simulation results, and the effectiveness of the proposed algorithm is verified. According to the relationship between the optimal value interval of the loss function and the training time, the optimal quantization series and the optimal bandwidth allocation strategy are obtained by solving the training time minimization problem. 2. For the user scheduling problem, the proposed user scheduling (TLM) scheme is compared with three other common scheduling schemes in the simulation, and the average precision is shown when the communication time is 6 000 s and 14 000 s, where the average Accuracy is obtained by measuring the IoU, the intersection of union, of the predicted value and the true value. The CA scheme yields the worst accuracy on car 1 with the largest channel attenuation, while the IA scheme exhibits the lowest accuracy on car 4 where the data is less important. The ICA scheme aims to find a balance between CA and IA, but due to its heuristic nature, the performance is lower than that of the TLM scheme.

Conclusions: 1. The training loss under the optimal quantization level and optimal bandwidth allocation reaches the predetermined threshold in a shorter time and can achieve the highest test accuracy. Secondly, the training performance under the non-optimal quantization level and optimal bandwidth allocation will be better than the performance of the optimal quantization leveland average bandwidth allocation, which also verifies the necessity of resource allocation. 2. The TLM scheme achieves slightly better performance early in training and significantly outperforms all other schemes after full training. This is due to the inherent prospective nature in the proposed TLM protocol which is advantageous over the myopic nature in the existing CA, IA and ICA protocols.

Key words: intelligent network towards 6G, task-oriented federated edge learning, resource management strategy, user scheduling strategy

CLC Number:

TN929.5

Zhiqin WANG, Jiamo JIANG, Peixi LIU, Xiaowen CAO, Yang LI, Kaifeng HAN, Ying DU, Guangxu ZHU. New design paradigm for federated edge learning towards 6G:task-oriented resource management strategies[J]. Journal on Communications, 2022, 43(6): 16-27.

Figures/Tables 10

References 51

[1]	IMT-2030（6G）推进组. 6G总体愿景与潜在关键技术白皮书[R]. 2021.
	IMT-2030 (6G) Promotion Group. White paper on 6G overall vision and potential key technology[R]. 2021.
[2]	张平, 牛凯, 田辉 ,等. 6G 移动通信技术展望[J]. 通信学报, 2019,40(1): 141-148.
	ZHANG P , NIU K , TIAN H ,et al. Technology prospect of 6G mobile communications[J]. Journal on Communications, 2019,40(1): 141-148.
[3]	LETAIEF K B , CHEN W , SHI Y ,et al. The roadmap to 6G:AI empowered wireless networks[J]. IEEE Communication Magazine, 2019,57(8): 84-90.
[4]	WANG Z Q , DU Y , WEI K J ,et al. Vision,application scenarios,and key technology trends for 6G mobile communications[J]. Science China Information Sciences, 2022,65(5): 1-27.
[5]	ZHU G X , LIU D Z , DU Y Q ,et al. Toward an intelligent edge:wireless communication meets machine learning[J]. IEEE Communications Magazine, 2020,58(1): 19-25.
[6]	LI E , ZENG L , ZHOU Z ,et al. Edge AI:On-demand accelerating deep neural network inference via edge computing[J]. IEEE Transaction on Wireless Communication, 2020,19(1): 447-457.
[7]	ZHU G X , XU J , HUANG K B ,et al. Over-the-air computing for wireless data aggregation in massive IoT[J]. IEEE Wireless Communications, 2021,28(4): 57-65.
[8]	KONECNY J , MCMAHAN H B , YU F X ,et al. Federated learning:strategies for improving communication efficiency[J]. arXiv Preprint,arXiv:1610.05492, 2016.
[9]	YANG Q , LIU Y , CHEN T ,et al. Federated machine learning:concept and applications[J]. ACM Transaction on Intelligent Systems, 2019,10(2): 1-19.
[10]	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.
[11]	DAYAN I , ROTH H R , ZHONG A X ,et al. Federated learning for predicting clinical outcomes in patients with COVID-19[J]. Nature Medicine, 2021,27(10): 1735-1743.
[12]	SAVAZZI S , NICOLI M , BENNIS M ,et al. Opportunities of federated learning in connected,cooperative,and automated industrial systems[J]. IEEE Communications Magazine, 2021,59(2): 16-21.
[13]	MILLS J , HU J , MIN G Y . Communication-efficient federated learning for wireless edge intelligence in IoT[J]. IEEE Internet of Things Journal, 2020,7(7): 5986-5994.
[14]	LAN Q , WEN D Z , ZHANG Z Z ,et al. What is semantic communication? A view on conveying meaning in the era of machine intelligence[J]. arXiv Preprint,arXiv:2110.00196, 2021.
[15]	TSE D , VISWANATH P . Fundamentals of wireless communication[M]. Cambridge: Cambridge University Press, 2005.
[16]	SHEN C , XU J , ZHENG S H ,et al. Resource rationing for wireless federated learning:concept,benefits,and challenges[J]. IEEE Communications Magazine, 2021,59(5): 82-87.
[17]	CHEN M Z , YANG Z H , SAAD W ,et al. A joint learning and communications framework for federated learning over wireless networks[J]. IEEE Transactions on Wireless Communications, 2021,20(1): 269-283.
[18]	WANG Y M , XU Y Q , SHI Q J ,et al. Quantized federated learning under transmission delay and outage constraints[J]. IEEE Journal on Selected Areas in Communications, 2022,40(1): 323-341.
[19]	ZHU G X , WANG Y , HUANG K B . Broadband analog aggregation for low-latency federated edge learning[J]. IEEE Transactions on Wireless Communications, 2020,19(1): 491-506.
[20]	LUO R Y , TIAN H , NI W L . Communication-aware path design for indoor robots exploiting federated deep reinforcement learning[C]// Proceedings of 2021 IEEE 32nd Annual International Symposium on Personal,Indoor and Mobile Radio Communications. Piscataway:IEEE Press, 2021: 1197-1202.
[21]	WAN S , LU J X , FAN P Y ,et al. Convergence analysis and system design for federated learning over wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2021,39(12): 3622-3639.
[22]	CHEN M Z , POOR H V , SAAD W ,et al. Convergence time optimization for federated learning over wireless networks[J]. IEEE Transactions on Wireless Communications, 2021,20(4): 2457-2471.
[23]	REN J Y , SUN J S , TIAN H ,et al. Joint resource allocation for efficient federated learning in Internet of Things supported by edge computing[C]// Proceedings of 2021 IEEE International Conference on Communications Workshops. Piscataway:IEEE Press, 2021: 1-6.
[24]	MCMAHAN B , MOORE E , RAMAGE D ,et al. Communication-efficient learning of deep networks from decentralized data[C]// Artificial Intelligence and Statistics. New York:PMLR, 2017: 1273-1282.
[25]	YANG H H , LIU Z Z , QUEK T Q S ,et al. Scheduling policies for federated learning in wireless networks[J]. IEEE Transactions on Communications, 2020,68(1): 317-333.
[26]	TAIK A , MLIKA Z , CHERKAOUI S . Data-aware device scheduling for federated edge learning[J]. IEEE Transactions on Cognitive Communications and Networking, 2022,8(1): 408-421.
[27]	WANG S Q , TUOR T , SALONIDIS T ,et al. Adaptive federated learning in resource constrained edge computing systems[J]. IEEE Journal on Selected Areas in Communications, 2019,37(6): 1205-1221.
[28]	RIZK E , VLASKI S , SAYED A H . Optimal importance sampling for federated learning[C]// Proceedings of 2021 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2021: 3095-3099.
[29]	AMIRI M M , GüNDüZ D , . Federated learning over wireless fading channels[J]. IEEE Transactions on Wireless Communications, 2020,19(5): 3546-3557.
[30]	KONECNY J , MCMAHAN H B , YU F X ,et al. Federated learning:strategies for improving communication and efficiency[J]. arXiv Preprint,arXiv:1610.05492, 2016.
[31]	AMIRI M M , GUNDUZ D , KULKARNI S R ,et al. Federated learning with quantized global model updates[J]. arXiv Preprint,arXiv:2006.10672, 2020.
[32]	NORI M K , YUN S , KIM I M . Fast federated learning by balancing communication trade-offs[J]. IEEE Transactions on Communications, 2021,69(8): 5168-5182.
[33]	唐伦, 汪智平, 蒲昊 ,等. 基于自适应梯度压缩的高效联邦学习通信机制研究[J]. 电子与信息学报， 2022:doi.org/10.11999/JEIT211262.
	TANG L , WANG Z P , PU H ,et al. Research on efficient federated learning communication mechanism based on adaptive gradient compression[J]. Journal of Electronics ＆ Information Technology， 2022:doi.org/10.11999/JEIT211262.
[34]	BOUZINIS P S , DIAMANTOULAKIS P D , KARAGIANNIDIS G K . Wireless quantized federated learning:a joint computation and communication design[J]. arXiv Preprint,arXiv:2203.05878, 2022.
[35]	ZHU G X , DU Y Q , GüNDüZ D , ,et al. One-bit over-the-air aggregation for communication-efficient federated edge learning:design and convergence analysis[J]. IEEE Transactions on Wireless Communications, 2021,20(3): 2120-2135.
[36]	ALISTARH D , GRUBIC D , LI J ,et al. QSGD:communication-efficient SGD via gradient quantization and encoding[C]// Advances in Neural Information Process.Systems. Massachusetts:MIT Press, 2017: 1707-1718.
[37]	LIU P X , JIANG J M , ZHU G X ,et al. Training time minimization for federated edge learning with optimized gradient quantization and bandwidth allocation[J]. arXiv Preprint,arXiv:2112.14387, 2021.
[38]	RAZAVIYAYN M . Successive convex approximation:analysis and applications[D]. TwenCities:University of Minnesota, 2014.
[39]	AMIRI M M , GüNDüZ D , KULKARNI S R ,et al. Convergence of update aware device scheduling for federated learning at the wireless edge[J]. IEEE Transactions on Wireless Communications, 2021,20(6): 3643-3658.
[40]	REN J K , HE Y H , WEN D Z ,et al. Scheduling for cellular federated edge learning with importance and channel awareness[J]. IEEE Transactions on Wireless Communications, 2020,19(11): 7690-7703.
[41]	YANG H H , ARAFA A , QUEK T Q S ,et al. Age-based scheduling policy for federated learning in mobile edge networks[C]// Proceedings of 2020 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2020: 8743-8747.
[42]	NISHIO T , YONETANI R . Client selection for federated learning with heterogeneous resources in mobile edge[C]// Proceedings of 2019 IEEE International Conference on Communications. Piscataway:IEEE Press, 2019: 1-7.
[43]	贺文晨, 郭少勇, 邱雪松 ,等. 基于DRL的联邦学习节点选择方法[J]. 通信学报, 2021,42(6): 62-71.
	HE W , GUO S , QIU X ,et al. Node selection method in federated learning based on deep reinforcement learning[J]. Journal on Communications, 2021,42(6): 62-71.
[44]	ZHANG M J , ZHU G X , WANG S ,et al. Accelerating federated edge learning via optimized probabilistic device scheduling[C]// Proceedings of 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications. Piscataway:IEEE Press, 2021: 606-610.
[45]	DOSOVITSKIY A , ROS G , CODEVILLA F ,et al. CARLA:an open urban driving simulator[J]. arXiv Preprint,arXiv:1711.03938, 2017.
[46]	YAN Y , MAO Y X , LI B . SECOND:sparsely embedded convolutional detection[J]. Sensors, 2018,18(10): 3337.
[47]	ZHANG Z J , WANG S , HONG Y C ,et al. Distributed dynamic map fusion via federated learning for intelligent networked vehicles[C]// Proceedings of 2021 IEEE International Conference on Robotics and Automation. Piscataway:IEEE Press, 2021: 953-959.
[48]	ABARI O , RAHUL H , KATABI D . Over-the-air function computation in sensor networks[J]. arXiv Preprint,arXiv:1612.02307, 2016.
[49]	JEON S , WANG C , GASTPAR M . Computation over Gaussian networks with orthogonal components[J]. IEEE Transaction on Information Theory, 2014,60(12): 7841-7861.
[50]	YANG K , JIANG T , SHI Y M ,et al. Federated learning via over-the-air computation[J]. IEEE Transactions on Wireless Communications, 2020,19(3): 2022-2035.
[51]	IMT-2030（6G）推进组. 通信感知一体化技术研究报告[R]. 2021.
	IMT-2030 (6G) Promotion Group. Research report on integrated sensing and communications technology[R]. 2021.

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