Robust searching-based gradient collaborative management in intelligent transportation system
With the rapid development of big data and the Internet of Things (IoT), traffic data from an Intelligent Transportation System (ITS) is becoming more and more accessible. To understand and simulate the traffic patterns from the traffic data, Multimedia Cognitive Computing (MCC) is an efficient and...
Published in: | ACM Transactions on Multimedia Computing, Communications, and Applications |
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Online Access: | https://pure.qub.ac.uk/en/publications/8b1f05fb-14b2-4080-bbc7-c968b5a65ae4 https://doi.org/10.1145/3549939 |
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ftqueensubelpubl:oai:pure.qub.ac.uk/portal:publications/8b1f05fb-14b2-4080-bbc7-c968b5a65ae4 2024-09-30T14:34:09+00:00 Robust searching-based gradient collaborative management in intelligent transportation system Shi, Hongjian Wang, Hao Ma, Ruhui Hua, Yang Song, Tao Gao, Honghao Guan, Haibing 2023-09-27 https://pure.qub.ac.uk/en/publications/8b1f05fb-14b2-4080-bbc7-c968b5a65ae4 https://doi.org/10.1145/3549939 eng eng https://pure.qub.ac.uk/en/publications/8b1f05fb-14b2-4080-bbc7-c968b5a65ae4 info:eu-repo/semantics/closedAccess Shi , H , Wang , H , Ma , R , Hua , Y , Song , T , Gao , H & Guan , H 2023 , ' Robust searching-based gradient collaborative management in intelligent transportation system ' , ACM Transactions on Multimedia Computing, Communications and Applications , vol. 20 , no. 2 , 34 . https://doi.org/10.1145/3549939 All-reduce collaborative management communication scheduling gradient aggregation robustness /dk/atira/pure/subjectarea/asjc/1700/1708 name=Hardware and Architecture /dk/atira/pure/subjectarea/asjc/1700/1705 name=Computer Networks and Communications article 2023 ftqueensubelpubl https://doi.org/10.1145/3549939 2024-09-05T00:49:03Z With the rapid development of big data and the Internet of Things (IoT), traffic data from an Intelligent Transportation System (ITS) is becoming more and more accessible. To understand and simulate the traffic patterns from the traffic data, Multimedia Cognitive Computing (MCC) is an efficient and practical approach. Distributed Machine Learning (DML) has been the trend to provide sufficient computing resources and efficiency for MCC tasks to handle massive data and complex models. DML can speed up computation with those computing resources but introduces communication overhead. Gradient collaborative management or gradient aggregation in DML for MCC tasks is a critical task. An efficient managing algorithm of the communication schedules for gradient aggregation in ITS can improve the performance of MCC tasks. However, existing communication schedules typically rely on specific physical connection matrices, which have low robustness when a malfunction occurs. In this article, we propose Robust Searching-based Gradient Collaborative Management (RSGCM) in Intelligent Transportation System, a practical ring-based gradient managing algorithm for communication schedules across devices to deal with ITS malfunction. RSGCM provides solutions of communication schedules to various kinds of connection matrices with an acceptable amount of training time. Our experimental results have shown that RSGCM can deal with more varieties of connection matrices than existing state-of-the-art communication schedules. RSGCM also increases the robustness of ITS since it can restore the system's functionality in an acceptable time when device or connection breakdown happens. Article in Journal/Newspaper DML Queen's University Belfast Research Portal ACM Transactions on Multimedia Computing, Communications, and Applications 20 2 1 23 |
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Open Polar |
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Queen's University Belfast Research Portal |
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ftqueensubelpubl |
language |
English |
topic |
All-reduce collaborative management communication scheduling gradient aggregation robustness /dk/atira/pure/subjectarea/asjc/1700/1708 name=Hardware and Architecture /dk/atira/pure/subjectarea/asjc/1700/1705 name=Computer Networks and Communications |
spellingShingle |
All-reduce collaborative management communication scheduling gradient aggregation robustness /dk/atira/pure/subjectarea/asjc/1700/1708 name=Hardware and Architecture /dk/atira/pure/subjectarea/asjc/1700/1705 name=Computer Networks and Communications Shi, Hongjian Wang, Hao Ma, Ruhui Hua, Yang Song, Tao Gao, Honghao Guan, Haibing Robust searching-based gradient collaborative management in intelligent transportation system |
topic_facet |
All-reduce collaborative management communication scheduling gradient aggregation robustness /dk/atira/pure/subjectarea/asjc/1700/1708 name=Hardware and Architecture /dk/atira/pure/subjectarea/asjc/1700/1705 name=Computer Networks and Communications |
description |
With the rapid development of big data and the Internet of Things (IoT), traffic data from an Intelligent Transportation System (ITS) is becoming more and more accessible. To understand and simulate the traffic patterns from the traffic data, Multimedia Cognitive Computing (MCC) is an efficient and practical approach. Distributed Machine Learning (DML) has been the trend to provide sufficient computing resources and efficiency for MCC tasks to handle massive data and complex models. DML can speed up computation with those computing resources but introduces communication overhead. Gradient collaborative management or gradient aggregation in DML for MCC tasks is a critical task. An efficient managing algorithm of the communication schedules for gradient aggregation in ITS can improve the performance of MCC tasks. However, existing communication schedules typically rely on specific physical connection matrices, which have low robustness when a malfunction occurs. In this article, we propose Robust Searching-based Gradient Collaborative Management (RSGCM) in Intelligent Transportation System, a practical ring-based gradient managing algorithm for communication schedules across devices to deal with ITS malfunction. RSGCM provides solutions of communication schedules to various kinds of connection matrices with an acceptable amount of training time. Our experimental results have shown that RSGCM can deal with more varieties of connection matrices than existing state-of-the-art communication schedules. RSGCM also increases the robustness of ITS since it can restore the system's functionality in an acceptable time when device or connection breakdown happens. |
format |
Article in Journal/Newspaper |
author |
Shi, Hongjian Wang, Hao Ma, Ruhui Hua, Yang Song, Tao Gao, Honghao Guan, Haibing |
author_facet |
Shi, Hongjian Wang, Hao Ma, Ruhui Hua, Yang Song, Tao Gao, Honghao Guan, Haibing |
author_sort |
Shi, Hongjian |
title |
Robust searching-based gradient collaborative management in intelligent transportation system |
title_short |
Robust searching-based gradient collaborative management in intelligent transportation system |
title_full |
Robust searching-based gradient collaborative management in intelligent transportation system |
title_fullStr |
Robust searching-based gradient collaborative management in intelligent transportation system |
title_full_unstemmed |
Robust searching-based gradient collaborative management in intelligent transportation system |
title_sort |
robust searching-based gradient collaborative management in intelligent transportation system |
publishDate |
2023 |
url |
https://pure.qub.ac.uk/en/publications/8b1f05fb-14b2-4080-bbc7-c968b5a65ae4 https://doi.org/10.1145/3549939 |
genre |
DML |
genre_facet |
DML |
op_source |
Shi , H , Wang , H , Ma , R , Hua , Y , Song , T , Gao , H & Guan , H 2023 , ' Robust searching-based gradient collaborative management in intelligent transportation system ' , ACM Transactions on Multimedia Computing, Communications and Applications , vol. 20 , no. 2 , 34 . https://doi.org/10.1145/3549939 |
op_relation |
https://pure.qub.ac.uk/en/publications/8b1f05fb-14b2-4080-bbc7-c968b5a65ae4 |
op_rights |
info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.1145/3549939 |
container_title |
ACM Transactions on Multimedia Computing, Communications, and Applications |
container_volume |
20 |
container_issue |
2 |
container_start_page |
1 |
op_container_end_page |
23 |
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1811637845040824320 |