[14] |
LI Y J , LIU I J , YUAN Y F ,et al. Accelerating distributed reinforcement learning with in-switch computing[C]// Proceedings of the 46th International Symposium on Computer Architecture. New York:ACM Press, 2019: 279-291.
|
[15] |
GRAHAM R L , BUREDDY D , LUI P ,et al. Scalable hierarchical aggregation protocol (SHArP):a hardware architecture for efficient data reduction[C]// Proceedings of 2016 First International Workshop on Communication Optimizations in HPC (COMHPC). Piscataway:IEEE Press, 2017: 1-10.
|
[1] |
新一代人工智能发展规划[R]. 2017.
|
|
The new generation of artificial intelligence development plan[R]. 2017.
|
[2] |
ABHISHEKVA , BINNY S , JOHAN T R ,et al. Federated learning:collaborative machine learning with out centralized training data[J]. International Journal of Engineering Technology and Management Sciences, 2022: 355-359.
|
[3] |
KAIROUZ P , MCMAHAN H B , AVENT B ,et al. Advances and open problems in federated learning[J]. Advances and Open Problems in Federated Learning, 2021.
|
[4] |
冯琦 . 基于安全多方计算的数据隐私保护技术研究[D]. 武汉:武汉大学, 2021.
|
|
FENG Q . Research on data privacy protection technology based on secure multi-party computing[D]. Wuhan:Wuhan University, 2021.
|
[5] |
LI M , ZHOU L , YANG Z ,et al. Parameter server for distributed machine learning[J]. Big Learning NIPS Workshop, 2013(6).
|
[6] |
LI M , ANDERSEN D G , PARK J W ,et al. Scaling distributed machine learning with the parameter server[C]// Proceedings of the 11th USENIX conference on Operating Systems Design and Implementation. New York:ACM Press, 2014: 583-598.
|
[7] |
薛镭, 邹涛, 张汝云 . 多模态智慧网络标准化研究[J]. 标准科学, 2021(S1): 37-48.
|
|
XUE L , ZOU T , ZHANG R Y . Standardization of multimodal smart network[J]. Standard Science, 2021(S1): 37-48.
|
[8] |
李丹, 胡宇翔, 邬江兴 . 新型网络技术创新发展战略研究[J]. 中国工程科学, 2021,23(2): 15-21.
|
|
LI D , HU Y X , WU J X . Innovative development strategy of new network technologies[J]. Strategic Study of CAE, 2021,23(2): 15-21.
|
[9] |
国兴昌 . 多模态网络中NDN与IP共存互通机制研究与实现[D]. 北京:北京交通大学, 2021.
|
|
GUO X C . Research and implementation of coexistence and interworking mechanism between NDN and IP in multi-modal networks[D]. Beijing:Beijing Jiaotong University, 2021.
|
[10] |
李军飞, 胡宇翔, 伊鹏 ,等. 面向 2035 的多模态智慧网络技术发展路线图[J]. 中国工程科学, 2020,22(3): 141-147.
|
|
LI J F , HU Y X , YI P ,et al. Development roadmap of polymorphic intelligence network technology toward 2035[J]. Strategic Study of CAE, 2020,22(3): 141-147.
|
[11] |
SAPIO A , CANINI M , HO C Y ,et al. Scaling distributed machine learning with in-network aggregation[EB]. arXiv Print, 2019,arXiv:1903.06701.
|
[12] |
LAO C L , LE Y , MAHAJAN K ,et al. ATP:in-network aggregation for multi-tenant learning[C]// In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). USENIX Association, 2021: 741-761.
|
[13] |
GEBARA N , GHOBADI M , COSTA P . In-network aggregation for shared machine learning clusters[J]. Proceedings of Machine Learning and Systems, 2021.
|