联邦学习通信优化方法综述

杨智凯; 刘亚萍; 张硕; 孙哲; 严定宇

doi:10.11959/j.issn.2096-109x.2024077

您当前的位置：

首页 >

文章列表页 >

联邦学习通信优化方法综述

综述 | 更新时间：2025-02-06

- 联邦学习通信优化方法综述
- Review of communication optimization methods in federated learning
- 网络与信息安全学报 2024年10卷第6期页码：1-23
- 作者机构：
  
  1.广州大学网络空间安全学院，广东广州 510006
  2.可信分布式计算与服务教育部重点实验室（北京邮电大学），北京 100876
- 作者简介：
  
  [ "杨智凯（1996- ），男，山西晋城人，广州大学博士生，主要研究方向为网络安全、联邦学习。" ]
  [ "刘亚萍（1973- ），女，湖南常德人，广州大学教授、博士生导师，主要研究方向为网络安全、数据中心网络、高性能网络、联邦学习。" ]
  [ "张硕（1984- ），男，湖北枣阳人，广州大学副教授、硕士生导师，主要研究方向为网络安全、数据中心网络、云计算、分布式系统。" ]
  [ "孙哲（1987- ），男，安徽安庆人，广州大学副教授、硕士生导师，主要研究方向为数据安全、隐私保护、人工智能安全。" ]
  [ "严定宇（1997- ），男，湖南常德人，北京邮电大学博士生，主要研究方向为网络安全、RDMA网络、数据中心网络。" ]
- 基金信息：
  
  国家自然科学基金(62372124);广东省重点领域研发计划(2019B010137005)
- DOI：10.11959/j.issn.2096-109x.2024077
  中图分类号： TP393
- 收稿：2024-05-27，
  
  修回：2024-11-05，
  
  纸质出版：2024-12-25
- 稿件说明：
移动端阅览
杨智凯, 刘亚萍, 张硕, 等. 联邦学习通信优化方法综述[J]. 网络与信息安全学报, 2024, 10(6): 1-23.

YANG Z K, LIU Y P, ZHANG S, et al. Review of communication optimization methods in federated learning[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 1-23.
杨智凯, 刘亚萍, 张硕, 等. 联邦学习通信优化方法综述[J]. 网络与信息安全学报, 2024, 10(6): 1-23. DOI： 10.11959/j.issn.2096-109x.2024077.

YANG Z K, LIU Y P, ZHANG S, et al. Review of communication optimization methods in federated learning[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 1-23. DOI： 10.11959/j.issn.2096-109x.2024077.

摘要

随着以深度学习为代表的人工智能技术的发展和普及，其不断暴露的安全问题已经成为影响网络空间安全的巨大挑战。传统的以云为中心的分布式机器学习通过收集参与方的数据来训练模型或优化模型表现，在数据交换过程中容易受到安全攻击和隐私攻击等网络安全风险的影响，进而造成整体系统效能下降或隐私数据泄露等后果。联邦学习作为一种具有隐私保护能力的分布式机器学习模式，通过客户端与参数服务器的频繁通信进行模型参数交换，在原始数据不离开本地的情况下训练联合模型，这极大降低了隐私数据泄露风险，在一定程度上保证了数据安全。但是随着深度学习模型的规模越来越大，联邦学习任务越来越复杂，通信开销也逐渐增加，最终成为联邦学习应用落地的阻碍。因此，针对联邦学习通信优化方法的探索成为联邦学习研究中的热点。介绍了联邦学习的技术背景和工作流程、分析了其通信瓶颈的来源和造成的影响。根据影响通信效率的因素，从模型参数压缩、模型更新策略、系统架构和通信协议等优化目标对现有联邦学习通信优化方法进行全面梳理和分析，并给出了该研究领域的发展脉络。最后总结了现有联邦学习通信优化方法所面临的问题，并对未来的发展趋势与研究方向进行展望。

Abstract

With the development and popularization of artificial intelligence technologies represented by deep learning

the security issues they continuously expose have become a huge challenge affecting cyberspace security. Traditional cloud-centric distributed machine learning

which trains models or optimizes model performance by collecting data from participating parties

is susceptible to security attacks and privacy attacks during the data exchange process

leading to consequences such as a decline in overall system efficiency or the leakage of private data. Federated learning

as a distributed machine learning paradigm with privacy protection capabilities

exchanges model parameters through frequent communication between clients and parameter servers

training a joint model without the raw data leaving the local area. This greatly reduces the risk of private data leakage and ensures data security to a certain extent. However

as deep learning models become larger and federated learning tasks more complex

communication overhead also increases

eventually becoming a barrier to the application of federated learning. Therefore

the exploration of communication optimization methods for federated learning has become a hot topic. The technical background and workflow of federated learning were introduced

and the sources and impacts of its communication bottlenecks were analyzed. Then

based on the factors affecting communication efficiency

existing federated learning communication optimization methods were comprehensively sorted out and analyzed from optimization objectives such as model parameter compression

model update strategies

system architecture

and communication protocols. The development trend of this research field was also presented. Finally

the problems faced by existing federated learning communication optimization methods were summarized

and future development trends and research directions were looked forward to.

关键词

Keywords

references

李凤华 , 李晖 , 贾焰 , 等 . 隐私计算研究范畴及发展趋势 [J ] . 通信学报 , 2016 , 37 ( 4 ): 1 - 11 .

LI F H , LI H , JIA Y , et al . Privacy computing: concept, connotation and its research trend [J ] . Journal on Communications , 2016 , 37 ( 4 ): 1 - 11 .

CUSTERS B , SEARS A M , DECHESNE F , et al . EU personal data protection in policy and practice [J ] . Information Technology and Law Series , 2019 .

李昕 , 唐鹏 , 张西珩 , 等 . 面向GDPR隐私政策合规性的智能化检测方法 [J ] . 网络与信息安全学报 , 2023 , 9 ( 6 ): 127 - 139 .

LI X , TANG P , ZHANG X Y , et al . GDPR-oriented intelligent checking method of privacy policies compliance [J ] . Chinese Journal of Network and Information Security , 2023 , 9 ( 6 ): 127 - 139 .

MCMAHAN H B , MORRE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [C ] // International Conference on Artificial Intelligence and Statistics , 2017 , 54 : 1273 - 1282 .

HARD A , RAO K , MATHEWS R , et al . Federated learning for mobile keyboard prediction [J ] . Computing Research Repository , 2018 , abs/1811.03604.

ZHENG W , YAN L , GAO C , et al . Federated meta-learning for fraudulent credit card detection [C ] // International Joint Conference on Artificial Intelligence , 2020 : 4654 - 4660 .

STEFANIE W , HARTMUT S , KRISHNAPRASAD L S , et al . Swarm learning for decentralized and confidential clinical machine learning [J ] . Nature , 2021 , 594 ( 7862 ): 265 - 270 .

SHELLER M J , EDWARDS B , REINA G A , et al . Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data [J ] . Scientific Reports , 2020 , 10 ( 1 ): 12598 .

DAYAN I , ROTH H R , ZHONG A , et al . Federated learning for predicting clinical outcomes in patients with COVID-19 [J ] . Nature Medicine , 2021 , 27 ( 10 ): 1735 - 1743 .

NGUYEN T D , MARCHAL S , MIETTINEN M , et al . DÏoT: a federated self-learning anomaly detection system for IoT [C ] // 2019 IEEE 39th International Conference on Distributed Computing Systems , 2019 : 756 - 767 .

ZHANG D , KOU Z , WANG D . FedSens: a federated learning approach for smart health sensing with class imbalance in resource constrained edge computing [C ] // IEEE Conference on Computer Communications , 2021 : 1 - 10 .

YE Y , ZHAO W , WEI T , et al . FedLight: federated reinforcement learning for autonomous multi-intersection traffic signal control [C ] // Design Automation Conference , 2021 : 847 - 852 .

YANG Q , LIU Y , TONG Y , et al . Federated machine learning: concept and applications [J ] . ACM Transactions on Intelligent Systems and Technology , 2019 , 10 ( 2 ): 12 : 1 - 12 : 19 .

ROY A G , SIDDIQUI S , POLSTERL S , et al . BrainTorrent: a peer-to-peer environment for decentralized federated learning [J ] . arXiv: Learning , 2019 , abs/1905. 06731 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [J ] . IEEE Computer Society Conference on Computer Vision and Pattern Recognition , 2016 , abs/1512.03385( 1 ): 770 - 778 .

MOTHUKURI V , PARIZI R M , POURIYEH S , et al . A survey on security and privacy of federated learning [J ] . Future Generation Computer Systems , 2021 , 115 : 619 - 640 .

SHI Y , YANG K , JIANG T , et al . Communication-efficient edge AI: algorithms and systems [J ] . IEEE Communications Surveys and Tutorials , 2020 , 22 ( 4 ): 2167 - 2191 .

ZHU H , XU J , LIU S , et al . Federated learning on non-IID data: a survey [J ] . Neurocomputing , 2021 , 465 : 371 - 390 .

YANN L , DENKER J S , SOLLA S A . Optimal brain damage [C ] // Conference on Neural Information Processing Systems , 1989 : 598 - 605 .

DENIL M , SHAKIBI B , DINH L , et al . Predicting parameters in deep learning [C ] // Conference on Neural Information Processing Systems , 2014 , abs/ 1306 . 0543 : 2148 - 2156 .

HANSON S J , PRATT L Y . Comparing biases for minimal network construction with back-propagation [J ] . Advances in Neural Information Processing Systems , 1988 , 1 : 177 - 185 .

LI H , KADAV A , DURDANOVIC L , et al . Pruning filters for efficient ConvNets [C ] // International Conference on Learning Representations , 2017 , abs/1608.08710.

LUO J , WU J , LIN W . ThiNet: a filter level pruning method for deep neural network compression [C ] // 2017 IEEE International Conference on Computer Vision , 2017 , abs/ 1707 . 06342 : 5068 - 5076 .

ZHOU H , ALVAREZ J M , PORIKIL F . Less is more: Towards compact CNNs [J ] . Lecture Notes in Computer Science , 2016 , 9908 : 662 - 677 .

HAN S , POOL J , TRAN J , et al . Learning both weights and connections for efficient neural network [J ] . Advances in Neural Information Processing Systems , 2015 , 28 : 1135 - 1143 .

ZHOU A , MA Y , ZHU J , et al . Learning N:M fine-grained structured sparse neural networks from scratch [C ] // International Conference on Learning Representations , 2021 , abs/2102.04010.

KONEČNÝ J , MCMAHAN H B , YU F X , et al . Federated learning: strategies for improving communication efficiency [J ] . Computing Research Repository , 2016 , abs/1610.05492.

CALDAS S , KONEČNÝ J , MCMAHAN H B , et al . Expanding the reach of federated learning by reducing client resource requirements [J ] . Computing Research Repository , 2018 , abs/1812.07210.

CHEN Y , CHEN Z , YU P , et al . FEDOBD: opportunistic block dropout for efficiently training large-scale neural networks through federated learning [C ] // International Joint Conference on Artificial Intelligence , 2023 : 3541 - 3549 .

JIANG X , BORCEA C . Complement sparsification: low-overhead model pruning for federated learning [C ] // AAAI Conference on Artificial Intelligence , 2023 , 37 ( 7 ): 8087 - 8095 .

LIAO D , GAO X , ZHAO Y , et al . Adaptive channel sparsity for federated learning under system heterogeneity [C ] // IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023 : 20432 - 20441 .

BIBIKAR S , VIKALO H , WANG Z , et al . Federated dynamic sparse training: computing less, communicating less, yet learning better [C ] // AAAI Conference on Artificial Intelligence , 2022 , abs/ 2112 . 09824 : 6080 - 6088 .

WANG I , NAIR P J , MAHAJAN D . FLuID: mitigating stragglers in federated learning using invariant dropout [C ] // Annual Conference on Neural Information Processing Systems , 2023 , abs/2307.02623.

ZHU Z , HONG J , DREW S , et al . Resilient and communication efficient learning for heterogeneous federated systems [C ] // International Conference on Machine Learning , 2022 , 162 : 27504 - 27526 .

SRINIVAS S , BABU R V . Data-free parameter pruning for deep neural networks [C ] // British Machine Vision Conference , 2015 , abs/1507.06149.

LIN Y , HAN S , MAO H , et al . Deep gradient compression: reducing the communication bandwidth for distributed training [C ] // International Conference on Learning Representations , 2017 , abs/1712.01887.

Li Z , RICHTÁRIK P . CANITA: faster rates for distributed convex optimization with communication compression [C ] // Conference on Neural Information Processing Systems , 2021 , abs/ 2107 . 09461 : 13770 - 13781 .

ROTHCHILD D , PANDA A , ULLAH E , et al . FetchSGD: communication-efficient federated learning with sketching [C ] // International Conference on Machine Learning , 2020 , abs/ 2007 . 07682 : 8253 - 8265 .

JHUNJHUNWALA D , MALLICK A , GADHIKAR A , et al . Leveraging spatial and temporal correlations in sparsified mean estimation [C ] // Conference on Neural Information Processing Systems , 2021 , abs/ 2110 . 07751 : 14280 - 14292 .

ZHENG F , CHEN C , LYU L , et al . Reducing communication for split learning by randomized Top- k sparsification [C ] // International Joint Conference on Artificial Intelligence , 2023 , abs/ 2305 . 18469 : 4665 - 4673 .

WU D , ZOU X , ZHANG S , et al . SmartIdx: reducing communication cost in federated learning by exploiting the CNNs structures [C ] // AAAI Conference on Artificial Intelligence , 2022 , 36 ( 4 ): 4254 - 4262 .

QIU X , FERNANDEZ-MARQUES J , GUSMAO P P , et al . ZeroFL: efficient on-device training for federated learning with local sparsity [C ] // International Conference on Learning Representations , 2022 .

KERKOUCHE R , ÁCS G , CASTELLUCCIA C , et al . Constrained differentially private federated learning for low-bandwidth devices [C ] // Conference on Uncertainty in Artificial Intelligence , 2021 , abs/ 2103 . 00342 : 1756 - 1765 .

JIANG Y , WANG S , KO B J , et al . Model pruning enables efficient federated learning on edge devices [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2019 , abs/ 1909 . 12326 ( 12 ): 10374 - 10386 .

JIANG Z , XU Y , XU H , et al . FedMP: federated learning through adaptive model pruning in heterogeneous edge computing [J ] . Proceedings. International Conference on Data Engineering , 2022 : 767 - 779 .

SHAH S M , LAU V K N . Model compression for communication efficient federated learning [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2023 , 34 ( 9 ): 5937 - 5951 .

XU Z , YU F , XIONG J , et al . Helios: Heterogeneity-aware federated learning with dynamically balanced collaboration [C ] // Design Automation Conference , 2021 , 2021 : 997 - 1002 .

ZHANG D , YANG J , YE D , et al . Lq-nets: Learned quantization for highly accurate and compact deep neural networks [J ] . Computer Vision - ECCV 2018 , PT VIII, 2018 , 11212 : 373 - 390 .

BANNER R , NAHSHAN Y , SOUDRY D . Post training 4-bit quantization of convolutional networks for rapid-deployment [J ] . Advances in Neural Information Processing Systems , 2019 , 32 : 7948 - 7956 .

COURBARIAUX M , BENGIO Y , DAVID J P . Binary connect: training deep neural networks with binary weights during propagations [J ] . Advances in Neural Information Processing Systems , 2015 , 28 : 3123 - 3131 .

COURBARIAUX M , HUBARA I , SOUDRY D , et al . Binarized neural networks: Training deep neural networks with weights and activations constrained tot 1 or 1 [J ] . arXiv: Learning , 2016 .

LI F , LIU B . Ternary weight networks [J ] . IEEE International Conference on Acoustics, Speech and Signal Processing , 2016 , abs/1605. 04711 : 1 - 5 .

SATTLER F , WIEDEMANN S , MÜLLER K , et al . Sparse binary compression: towards distributed deep learning with minimal communication [J ] . 2019 International Joint Conference on Neural Networks , 2018 , abs/1805. 08768 : 1 - 8 .

SATTLER F , WIEDEMANN S , MULLER K , et al . Robust and communication-efficient federated learning from Non-I.I.D. data [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2019 , ( 99 ): 1 - 14 .

XU J , DU W , JIN Y , et al . Ternary compression for communication-efficient federated learning [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 , 33 ( 3 ): 1162 - 1176 .

REISIZADEH A , MOKHTARI A , HASSANI H , et al . FedPAQ: a communication-efficient federated learning method with periodic averaging and quantization [C ] // International Conference on Artificial Intelligence and Statistics , 2019 , 108 : 2021 - 2031 .

AMIRI M M , GUNDUZ D , KULKARNI S R , et al . Federated learning with quantized global model updates [J ] . Openalex , 2020 , abs/2006. 10672 .

MILLS J , HU J , MIN G . Communication-efficient federated learning for wireless edge intelligence in IoT [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 7 ): 5986 - 5994 .

SHLEZINGER N , CHEN M , ELDAR Y C , et al . Federated learning with quantization constraints [C ] // IEEE International Conference on Acoustics, Speech, and Signal Processing , 2020 : 8851 - 8855 .

JIN H , WU D , ZHANG S , et al . Design of a quantization-based DNN delta compression framework for model snapshots and federated learning [J ] . IEEE Transactions on Parallel and Distributed Systems , 2023 , 34 ( 3 ): 923 - 937 .

HADDADPOUR F , KAMANI M M , MOKHTARI A , et al . Federated learning with compression: unified analysis and sharp guarantees [C ] // International Conference on Artificial Intelligence and Statistics , 2020 , 130 : 2350 - 2358 .

HOENIG R , ZHAO Y , MULLINS R . DAdaQuant: doubly-adaptive quantization for communication-efficient federated fearning [C ] // International Conference on Machine Learning , 2022 , abs/ 2111 . 00465 : 8852 - 8866 .

LIU H , HE F , CAO G . Communication-efficient federated learning for heterogeneous edge devices based on adaptive gradient quantization [C ] . IEEE Conference on Computer Communications , 2022 : 1 - 10 .

VARGAFTIK S , BASAT R B , PORTNOY A , et al . EDEN: communication-efficient and robust distributed mean estimation for federated learning [C ] // International Conference on Machine Learning , 2022 : 21984 - 22014 .

SUN J , CHEN T , GIANNAKIS G B , et al . Lazily aggregated quantized gradient innovation for communication-efficient federated learning [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 4 ): 2031 - 2044 .

HINTON G E , VINYALS O , DEAN J . Distilling the knowledge in a neural network [J ] . arXiv: Machine Learning , 2015 , abs/1503.02531.

JEONG E , OH S , KIM H , et al . Communication-efficient on-device machine learning: federated distillation and augmentation under Non-IID private data [J ] . Computing Research Repository , 2018 , abs/1811.11479.

BISTRITZ I , MANN A , BAMBOS N . Distributed distillation for on-device learning [C ] // Conference on Neural Information Processing Systems , 2020 , 33 : 22593 - 22604 .

WU C , WU F , LYU L , et al . Communication-efficient federated learning via knowledge distillation [J ] . Nature Communications , 2022 , 13 ( 1 ): 2032 - 8 .

ITAHARA S , NISHIO T , KODA Y , et al . Distillation-based semi-supervised federated learning for communication-efficient collaborative training with Non-IID private data [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 1 ): 191 - 205 .

HE C , ANNAVARAM M , AVESTIMEHR S . Group knowledge transfer: federated learning of large CNNs at the edge [C ] // Conference on Neural Information Processing Systems , 2020 , 33 : 14068 - 14080 .

HAMER J , MOHRI M , SURESH A T . FedBoost: communication-efficient algorithms for federated learning [C ] // International Conference on Machine Learning , 2020 , 119 : 3973 - 3983 .

ALMEIDA A , FAVIER G , MOTA J C M . Tensor decompositions and applications to wireless communication systems [C ] // IEEE International Telecommunications Symposium , 2006 .

VOGELS T , KARINIREDDY S P , JAGGI M . Powersgd: practical low-rank gradient compression for distributed optimization [J ] . Advances in Neural Information Processing Systems , 2019 , 32 : 14236 - 14245 .

NAM H , MOON Y , OH T . FedPara: low-rank hadamard product for communication-efficient federated learning [C ] // International Conference on Learning Representations , 2022 .

CHEN B , BAKHSHI ALI , BATISTA G , et al . Update compression for deep neural networks on the edge [C ] // Computer Vision and Pattern Recognition , 2022 : 3075 - 3085 .

WANG H , LIU X , NIU J , et al . SVDFed: enabling communication-efficient federated learning via singular-value-decomposition [C ] // IEEE Conference on Computer Communications , 2023 : 1 - 10 .

AZAM S S , HOSSEINALIPOUR S , QIU Q , et al . Recycling model updates in federated learning: are gradient subspaces low-rank [C ] // International Conference on Learning Representations , 2022 .

GUAN Y , LIU X , REN T , et al . Enabling communication-efficient federated learning via distributed compressed sensing [C ] // IEEE Conference on Computer Communications , 2023 : 1 - 10 .

SONG Z , WANG Y , YU Z , et al . Sketching for first order method: efficient algorithm for low-bandwidth channel and vulnerability [C ] // International Conference on Machine Learning , 2023 : 32365 - 32417 .

JIANG S , SHARMA P , JOSHI G . Correlation aware sparsified mean estimation using random projection [C ] // Conference on Neural Information Processing Systems , 2023 , abs/2310.18868.

ZHU L , LIN H , LU Y , et al . Delayed gradient averaging: tolerate the communication latency for federated learning [C ] // Conference on Neural Information Processing Systems , 2021 , 34 : 29995 - 30007 .

LI L , SHI D , HOU R , et al . To talk or to work: flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices [C ] // IEEE Conference on Computer Communications , 2021 , abs/ 2012 . 11804 : 1 - 10 .

WANG J , JOSHI G . Cooperative SGD: A unified framework for the design and analysis of local-update SGD algorithms [J ] . Journal of Machine Learning Research , 2021 , 22 ( 1 ): 1 - 50 .

CHEN Y , SUN X , JIN Y . Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2020 , 31 ( 10 ): 4229 - 4238 .

WU W , HE L , LIN W , et al . SAFA: A Semi-asynchronous protocol for fast federated learning with low overhead [J ] . IEEE Transactions on Computers , 2021 , 70 ( 5 ): 655 - 668 .

ZHANG F , LIU X , LIN S , et al . No one idles: efficient heterogeneous federated learning with parallel edge and server computation [C ] // International Conference on Machine Learning , 2023 : 41399 - 41413 .

WANG H , XU W , FAN Y , et al . AOCC-FL: Federated learning with aligned overlapping via calibrated compensation [C ] // IEEE Conference on Computer Communications , 2023 : 1 - 10 .

CHEN R , WAN Q , PRAKASH P , et al . Workie-Talkie: accelerating federated learning by overlapping computing and communications via contrastive regularization [C ] // IEEE International Conference on Computer Vision , 2023 : 16953 - 16963 .

ZHOU Y , QING Y , LV J . Communication-efficient federated learning with compensated overlap-FedAvg [J ] . IEEE Transactions on Parallel and Distributed Systems , 2022 , 33 ( 1 ): 192 - 205 .

DORFMAN R , VARGAFTIK S , BEN-ITZHAK Y , et al . DoCoFL: downlink compression for cross-device federated learning [C ] // International Conference on Machine Learning , 2023 : 8356 - 8388 .

ZHANG Y , LIU D , DUAN M , et al . FedMDS: an efficient model discrepancy-aware semi-asynchronous clustered federated learning framework [J ] . IEEE Transactions on Parallel and Distributed Systems , 2023 , 34 ( 3 ): 1007 - 1019 .

BASU D , DATA D , KARAKUS C , et al . Qsparse-local-SGD: distributed SGD with quantization, sparsification, and local computations [J ] . IEEE Journal on Selected Areas in Information Theory , 2020 , 1 ( 1 ): 14668 - 14679 .

NISHIO T , YONETANI R . Client selection for federated learning with heterogeneous resources in mobile edge [C ] // 2019 IEEE International Conference on Communications , 2018 , abs/ 1804 . 08333 : 1 - 7 .

ZHANG S Q , LIN J , ZHANG Q . A Multi-agent reinforcement learning approach for efficient client selection in federated learning [C ] // AAAI Conference on Artificial Intelligence , 2022 : 9091 - 9099 .

ZHAO B , LIU Z , CHEN C , et al . Adaptive client sampling in federated learning via online learning with bandit feedback [C ] . International Conference on Learning Representations , 2021 , abs/2112.14332.

TANG M , NING X , WANG Y , et al . FedCor: correlation-based active client selection strategy for heterogeneous federated learning [J ] . Semanticscholar , 2022 ( 1 ): 10092 - 10101 .

JIANG Z , XU Y , XU H , et al . Heterogeneity-aware federated learning with adaptive client selection and gradient compression [C ] // IEEE Conference on Computer Communications , 2023 : 1 - 10 .

FRABONI Y , VIDAL R , KAMENI L , et al . Clustered sampling: low-variance and improved representativity for clients selection in federated learning [C ] // International Conference on Machine Learning , 2021 , 139 : 3407 - 3416 .

李群 , 陈思光 . 基于选择性通信策略的高效联邦学习研究 [J ] . 小型微型计算机系统 , 2024 , 45 ( 3 ): 549 - 554 .

LI Q , CHEN S G . FedSCP: efficient federated learning based on selective communication policy [J ] . Journal of Chinese Computer Systems , 2024 , 45 ( 3 ): 549 - 554 .

YI L , WANG G , LIU X . QSFL: A two-level uplink communication optimization framework for federated learning [C ] // International Conference on Machine Learning , 2022 : 25501 - 25513 .

LI L , DUAN M , LIU D , et al . FedSAE: A novel self-adaptive federated learning framework in heterogeneous systems [C ] // IEEE International Joint Conference on Neural Network , 2021 , abs/ 2104 . 07515 : 1 - 10 .

WANG C , YANG Y , ZHOU P . Towards Efficient scheduling of federated mobile devices under computational and statistical heterogeneity [J ] . IEEE Transactions on Parallel and Distributed Systems , 2021 , 32 ( 2 ): 394 - 410 .

BRIGGS C , FAN Z , ANDRAS P . Federated learning with hierarchical clustering of local updates to improve training on non-IID data [C ] . IEEE International Joint Conference on Neural Network , 2020 , abs/2004. 11791 : 1 - 9 .

WU W , HE L , LIN W , et al . Accelerating federated learning over reliability-agnostic clients in mobile edge computing systems [J ] . IEEE Transactions on Parallel and Distributed Systems , 2021 , 32 ( 7 ): 1539 - 1551 .

LUO S , CHEN X , WU Q , et al . HFEL: joint edge association and resource allocation for cost-efficient hierarchical federated edge learning [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 10 ): 6535 - 6548 .

WANG Z , XU H , LIU J , et al . Resource-efficient federated learning with hierarchical aggregation in edge computing [C ] . IEEE Conference on Computer Communications , 2021 : 1 - 10 .

QU Z , GUO S , WANG H , et al . Partial synchronization to accelerate federated learning over relay-assisted edge networks [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 12 ): 4502 - 4516 .

LIU L , ZHANG J , SONG S , et al . Hierarchical federated learning with quantization: convergence analysis and system design [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 1 ): 2 - 18 .

WU Q , CHEN X , TAO O , et al . HiFlash: communication-efficient hierarchical federated learning with adaptive staleness control and heterogeneity-aware client-edge association [J ] . IEEE Transactions on Parallel and Distributed Systems , 2023 , 34 ( 5 ): 1560 - 1579 .

MARFOQ O , XU C , NEGLIA G , et al . Throughput-optimal topology design for cross-silo federated learning [C ] // Conference on Neural Information Processing Systems , 2020 , 33 : 19478 - 19487 .

REISIZADEH A , TAHERI H , MOKHTARI A , et al . Robust and communication-efficient collaborative learning [J ] . Advances in Neural Information Processing Systems , 2019 , 32 : 8386 - 8397 .

TANG Z , SHI S , LI B , et al . GossipFL: a decentralized federated learning framework with sparsified and adaptive communication [J ] . IEEE Transactions on Parallel and Distributed Systems , 2023 , 34 ( 3 ): 909 - 922 .

HASHEMI A , ACHARYA A , DAS R , et al . On the benefits of multiple gossip steps in communication-constrained decentralized federated learning [J ] . IEEE Transactions on Parallel and Distributed Systems , 2022 , 33 ( 11 ): 2727 - 2739 .

GUO Y , SUN Y , HU R , et al . Hybrid local SGD for federated learning with heterogeneous communications [C ] // International Conference on Learning Representations , 2022 .

LIU J , XU Y , XU H , et al . Enhancing federated learning with intelligent model migration in heterogeneous edge computing [C ] // IEEE International Conference on Data Engineering , 2022 : 1586 - 1597 .

NGUYEN H T , BAO W , NGUYEN M N , et al . Federated learning over wireless networks: optimization model design and analysis [C ] // IEEE Conference on Computer Communications , 2019 : 1387 - 1395 .

LUO B , LI X , WANG S , et al . Cost-effective federated learning design [C ] // IEEE Conference on Computer Communications , 2021 , abs/ 2012 . 08336 : 1 - 10 .

CUI L , SU X , ZHOU Y , et al . Optimal rate adaption in federated learning with compressed communications [C ] // IEEE Conference on Computer Communications , 2022 , abs/ 2112 . 06694 : 1459 - 1468 .

ZHU G , WANG Y , HUANG K . Broadband analog aggregation for low-latency federated edge learning [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 1 ): 491 - 506 .

ZHANG J , LI N , DEDEOGLU M . Federated learning over wireless networks: a band-limited coordinated descent approach [C ] // IEEE Conference on Computer Communications , 2021 , abs/2102.07972 ( 2021 ): 1 - 10 .

NI W , LIU Y , YANG Z , et al . Over-the-air federated learning and non-orthogonal multiple access unified by reconfigurable intelligent surface [C ] // IEEE Conference on Computer Communications , 2021 : 1 - 6 .

LIU D . Accelerating intra-party communication in vertical federated learning with RDMA [C ] // Conference on Emerging Network Experiment and Technology , 2020 : 14 - 20 .

HEGDE P , VECIANA G D , MOKHTARI A . Network adaptive federated learning: congestion and lossy compression [C ] // IEEE Conference on Computer Communications , 2023 : 1 - 10 .

SU X , ZHOU Y , CUI L , et al . On model transmission strategies in federated learning with lossy communications [J ] . IEEE Transactions on Parallel and Distributed Systems , 2023 , 34 ( 4 ): 1173 - 1185 .

寇文龙 , 李凤华 , 董秀则 , 等 . 支持差异化可协商的数据通信机制 [J ] . 通信学报 , 2021 , 42 ( 10 ): 55 - 66 .

KOU W L , LI F H , DONG X Z , et al . Differentiated and negotiable mechanism for data communication [J ] . Journal on Communications , 2021 , 42 ( 10 ): 55 - 66 .

李凤华 , 李晖 , 牛犇 , 等 . 隐私计算的学术内涵与研究趋势 [J ] . 网络与信息安全学报 , 2022 , 8 ( 6 ): 1 - 8 .

LI F H , LI H , NIU B , et al . Academic connotation and research trends of privacy computing [J ] . Chinese Journal of Network and Information Security , 2022 , 8 ( 6 ): 1 - 8 .

李凤华 , 李晖 , 牛犇 , 等 . 数据要素流通与安全的研究范畴与未来发展趋势 [J ] . 通信学报 , 2024 , 45 ( 5 ): 1 - 11 .

LI F H , LI H , NIU B , et al . Research category and future development trend of data elements circulation and security [J ] . Journal on Communications , 2024 , 45 ( 5 ): 1 - 11 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

DP-FedDRC：缓解维度坍塌的差分隐私个性化联邦学习

轻量级软件定义边缘计算网络拓扑发现方法

基于动态聚类和信誉评估的抗攻击联邦框架

基于风险权值聚合联邦学习的金融欺诈检测模型

FL-IoV：基于联邦学习的综合车联网入侵检测方法