TDMA-based user scheduling policies for federated learning

Meixia TAO; Dong WANG; Rui SUN; Naifu ZHANG

doi:10.11959/j.issn.1000-436x.2021056

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TDMA-based user scheduling policies for federated learning

Papers | 更新时间：2024-06-05

- TDMA-based user scheduling policies for federated learning
- Journal on Communications Vol. 42, Issue 6, Pages: 1-29(2021)
- 作者机构：
  
  上海交通大学电子信息与电气工程学院，上海 200240
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2019YFB1802702);The National Natural Science Foundation of China(61941106)
- DOI：10.11959/j.issn.1000-436x.2021056
  CLC： TN929.5
- Online First：2021-06，
  
  Published：25 June 2021
- 稿件说明：
移动端阅览
Meixia TAO, Dong WANG, Rui SUN, et al. TDMA-based user scheduling policies for federated learning[J]. Journal on Communications, 2021, 42(6): 1-29.
DOI：

Meixia TAO, Dong WANG, Rui SUN, et al. TDMA-based user scheduling policies for federated learning[J]. Journal on Communications, 2021, 42(6): 1-29. DOI： 10.11959/j.issn.1000-436x.2021056.

摘要

为了提高联邦学习的通信效率，针对用户计算能力和信道状态异构的场景，提出了一类基于时分多址接入的用户调度策略，在满足给定单轮模型训练所需计算的样本数量约束下，最小化单轮模型更新的系统时延。理论分析了该调度策略的预期收敛速度，探究收敛性能与系统总时延的均衡关系，并进一步分析最优批大小的选择问题。仿真结果显示，所提算法与基准算法相比，模型收敛速率提升30%以上。

Abstract

To improve the communication efficiency in FL (federated learning)

for the scenario with heterogeneous edge user's computing capacity and channel state

a class of time division multiple access (TDMA) based user scheduling policies were proposed for FL.The proposed policies aim to minimize the system delay in each round of model training subject to a given sample size constraint required for computing in each round.In addition

the convergence rate of the proposed scheduling algorithms was analyzed from a theoretical perspective to study the tradeoff between the convergence performance and the total system delay.The selection of the optimal batch size was further analyzed.Simulation results show that the convergence rate of the proposed algorithm is at least 30% higher than all the considered benchmarks.

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references

HU Y C , PATEL M , SABELLA D , et al . Mobile edge computing—a key technology towards 5G [J ] . ETSI White Paper , 2015 , 11 ( 11 ): 1 - 16 .

PARK J , SAMARAKOON S , BENNIS M , et al . Wireless network intelligence at the edge [J ] . Proceedings of the IEEE , 2019 , 107 ( 11 ): 2204 - 2239 .

ZHOU Z , CHEN X , LI E , et al . Edge intelligence:paving the last mile of artificial intelligence with edge computing [J ] . Proceedings of the IEEE , 2019 , 107 ( 8 ): 1738 - 1762 .

TAO M X , HUANG K B . Editorial:special topic on machine learning at network edges [J ] . ZTE Communications , 2020 , 18 ( 2 ): 1 , 30 .

MCMAHAN B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [C ] // 2017 Artificial Intelligence and Statistics . Saarland:DBLP , 2017 : 1273 - 1282 .

ALISTARH D , GRUBIC D , LI J , et al . QSGD:communication-efficient SGD via randomized quantization and encoding [J ] . Advances in Neural Information Processing Systems , 2018 , 3 : 1710 - 1721 .

AJI A F , HEAFIELD K . Sparse communication for distributed gradient descent [J ] . arXiv Preprint,arXiv:1704.05021 , 2017 .

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 .

MOHAMMADI A M , GÜNDÜZ D . Machine learning at the wireless edge:distributed stochastic gradient descent over-the-air [J ] . IEEE Transactions on Signal Processing , 2020 , 68 : 2155 - 2169 .

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 .

ZHANG N F , TAO M X . Gradient statistics aware power control for over-the-air federated learning [J ] . arXiv Preprint,arXiv:2003.02089 , 2020 .

YANG H H , ARAFA A , QUEK T Q S , et al . Age-based scheduling policy for federated learning in mobile edge networks [C ] // 2020 IEEE International Conference on Acoustics . Piscataway:IEEE Press , 2020 : 8743 - 8747 .

NISHIO T , YONETANI R . Client selection for federated learning with heterogeneous resources in mobile edge [C ] // 2019 IEEE International Conference on Communications . Piscataway:IEEE Press , 2019 : 1 - 7 .

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 .

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 .

MA X , SUN H , HU R Q . Scheduling policy and power allocation for federated learning in NOMA based MEC [J ] . arXiv Preprint,arXiv:2006.13044 , 2020 .

AMIRIA M M , GÜNDÜZB D , KULKARNI S R , et al . Convergence of update aware device scheduling for federated learning at the wireless edge [J ] . arXiv Preprint,arXiv:2001.10402 , 2020 .

ZENG Q S , DU Y Q , HUANG K B , et al . Energy-efficient radio resource allocation for federated edge learning [C ] // 2020 IEEE International Conference on Communications Workshops . Piscataway:IEEE Press , 2020 : 1 - 6 .

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 .

SHI W Q , ZHOU S , NIU Z S , et al . Joint device scheduling and resource allocation for latency constrained wireless federated learning [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 1 ): 453 - 467 .

DEKEL O , GILAD-BACHRACH R , SHAMIR O , et al . Optimal distributed online prediction using mini-batches [J ] . The Journal of Machine Learning Research , 2012 , 13 : 165 - 202 .

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