Search Results - Semi-asynchronous distributed algorithm~

'Semi-Asynchronous': A New Scheduler in Distributed Computing

Authors: Serafino Cicerone Gabriele Di Stefano Alfredo Navarra

Source: IEEE Access, Vol 9, Pp 41540-41557 (2021)

Subject Terms: Distributed algorithms, gathering, mobile robots, synchronization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9373406/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/7fc5d2f47e13415a90181eedbced89e6

'Semi-Asynchronous': A New Scheduler for Robot Based Computing Systems

Authors: Cicerone, Serafino Di Stefano, Gabriele Navarra, Alfredo

Source: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, Distributed Algorithms, Mobile Robots, Obliviousness, Synchronization, Software, Hardware and Architecture, Computer Networks and Communications

Access URL: https://ieeexplore.ieee.org/document/8416290
https://doi.org/10.1109/ICDCS.2018.00027
https://research.unipg.it/handle/11391/1438301
https://dblp.uni-trier.de/db/conf/icdcs/icdcs2018.html#CiceroneSN18
https://hdl.handle.net/11391/1438301
https://ieeexplore.ieee.org/document/8416290
https://doi.org/10.1109/ICDCS.2018.00027
https://hdl.handle.net/11697/128056
https://doi.org/10.1109/ICDCS.2018.00027

Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network.

Authors: Mei, Qiang Huang, Rui Li, Duo et al.

Source: Autonomous Intelligent Systems; 3/20/2025, Vol. 5 Issue 1, p1-14, 14p

Subject Terms: FEDERATED learning, TELECOMMUNICATION satellites, DISTRIBUTED computing, SYNCHRONIZATION

Semi-asynchronous and distributed weighted connected dominating set algorithms for wireless sensor networks.

Authors: Dagdeviren, Orhan Erciyes, Kayhan Tse, Savio

Source: Computer Standards & Interfaces. Nov2015, Vol. 42, p143-156. 14p.

Subject Terms: *DISTRIBUTED computing, *SET theory, *WIRELESS sensor networks, *ALGORITHMS, *ENERGY consumption

SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters

Authors: Liu, Zhuqing Zhang, Xin Liu, Jia

Index Terms: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2208.08425

SACW: Semi-Asynchronous Federated Learning with Client Selection and Adaptive Weighting.

Authors: Li, Shuaifeng Shan, Fangfang Mao, Shiqi et al.

Source: Computers (2073-431X); Nov2025, Vol. 14 Issue 11, p464, 21p

Subject Terms: FEDERATED learning, HETEROGENEITY

SAFL-KCS: federated learning methods based on client selection and semi-asynchronous communication.

Authors: Shan, Fangfang Li, Shuaifeng Lu, Yanlong et al.

Source: Cluster Computing; 2025, Vol. 28 Issue 11, p1-15, 15p

Subject Terms: FEDERATED learning, HETEROGENEITY, K-means clustering, MACHINE-to-machine communications

A Distributed Optimization Method for the Geographically Distributed Data Centres Problem

Authors: Wahbi, Mohamed Grimes, Diarmuid Mehta, Deepak et al.

Contributors: Wahbi, Mohamed Grimes, Diarmuid Mehta, Deepak et al.

Source: Lecture Notes in Computer Science ISBN: 9783319597751

Subject Terms: Data centre, Semi-asynchronous distributed algorithm, Distributed optimization method, Distributed constraint optimization framework, 0202 electrical engineering, electronic engineering, information engineering, DCOP, 0102 computer and information sciences, 02 engineering and technology, Virtual machine, 7. Clean energy, 01 natural sciences, 3. Good health

File Description: application/pdf

Access URL: https://cora.ucc.ie/bitstream/10468/4548/1/2812.pdf
https://link.springer.com/chapter/10.1007/978-3-319-59776-8_12
https://doi.org/10.1007/978-3-319-59776-8_12
https://cora.ucc.ie/handle/10468/4548
http://doi.org/10.1007/978-3-319-59776-8_12
https://dblp.uni-trier.de/db/conf/cpaior/cpaior2017.html#WahbiGMBO17
http://hdl.handle.net/10468/4548

Dynamic density adaptive clustering and blockchain integration for efficient federated learning.

Authors: Wang, Jinlong Yang, Xingtao Zhao, Haoran et al.

Source: Pattern Recognition Letters. Nov2025, Vol. 197, p267-273. 7p.

Subject Terms: *FEDERATED learning, *BLOCKCHAINS, *CLUSTERING algorithms, *ROBUST optimization

Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT.

Authors: Fan, Boyu Su, Xiang Tarkoma, Sasu et al.

Source: ACM Transactions on Sensor Networks; May2024, Vol. 20 Issue 3, p1-28, 28p

Subject Terms: FEDERATED learning, MACHINE learning, DATA privacy, INTERNET of things, ASYNCHRONOUS learning, PROCESS capability, SUPERVISED learning

ASR-Fed: agnostic straggler-resilient semi-asynchronous federated learning technique for secured drone network.

Authors: Ihekoronye, Vivian Ukamaka Nwakanma, Cosmas Ifeanyi Kim, Dong-Seong et al.

Source: International Journal of Machine Learning & Cybernetics; Nov2024, Vol. 15 Issue 11, p5303-5319, 17p

An efficient aggregation algorithm based on synchronous-asynchronous mechanism for federated learning.

Authors: Mou, Yangcheng Chen, Aiwang Chen, Guirong et al.

Source: Scientific Reports; 11/19/2025, Vol. 15 Issue 1, p1-15, 15p

Subject Terms: FEDERATED learning, COMPUTER performance

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

Authors: Jianing Zheng Xiaolan Liu Zhuang Ling et al.

Source: IEEE Internet of Things Journal. 11:29673-29688

Efficient federated learning via aggregation of base models.

Authors: Wang, Pan Zhong, Zhengyi Wang, Ji

Source: PLoS ONE; 8/14/2025, Vol. 20 Issue 8, p1-23, 23p

Subject Terms: FEDERATED learning, MACHINE learning, EVOLUTIONARY algorithms, DATA distribution, INFORMATION networks, PRIVACY

An Experimental Study of Different Aggregation Schemes in Semi-Asynchronous Federated Learning

Authors: Li, Yunbo Gui, Jiaping Wu, Yue

Subject Terms: Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Performance, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)

Access URL: http://arxiv.org/abs/2405.16086

Joint device assignment and resource allocation for hierarchical semi-asynchronous federated learning over wireless networks.

Authors: Fan, Wei Li, Jing

Source: Cluster Computing; 2025, Vol. 28 Issue 8, p1-26, 26p

Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT

Authors: Boyu Fan Xiang Su Sasu Tarkoma et al.

Contributors: Boyu Fan Xiang Su Sasu Tarkoma et al.

Source: ACM Transactions on Sensor Networks. 20:1-28

Subject Terms: Smart sensing, Computer and information sciences, Federated learning, 0202 electrical engineering, electronic engineering, information engineering, Deep learning, Edge computing, 02 engineering and technology

File Description: application/pdf

Access URL: http://hdl.handle.net/10138/591757

FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing.

Authors: Ma, Qianpiao Xu, Yang Xu, Hongli et al.

Source: IEEE Journal on Selected Areas in Communications; Dec2021, Vol. 39 Issue 12, p3654-3672, 19p

Subject Terms: HETEROGENEOUS computing, EDGE computing, ALGORITHMS, DATA transmission systems, ASYNCHRONOUS learning, MACHINE learning

Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network

Authors: Qiang Mei Rui Huang Duo Li et al.

Source: Autonomous Intelligent Systems, Vol 5, Iss 1, Pp 1-14 (2025)

Subject Terms: Federated learning system, Cloud-edge-client structure, Satellite network, Intelligent control strategy, Electronic computers. Computer science, QA75.5-76.95, Computer engineering. Computer hardware, TK7885-7895

File Description: electronic resource

Relation: https://doaj.org/toc/2730-616X

Access URL: https://doaj.org/article/71d8081c4b474273b3fe8d7f364b7fc2

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks

Authors: Liu, Liang Zhai, Wenbin Wang, Feng et al.

Subject Terms: FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)

Access URL: http://arxiv.org/abs/2308.11981