Resource trading strategies with risk selection in collaborative training market.
The rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying economic mechanisms of resource trading. In this stu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0328625 |
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| _version_ | 1849304228640587776 |
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| author | Quyuan Wang Xinyu Ni Yuping Tu Ying Wang Jiadi Liu |
| author_facet | Quyuan Wang Xinyu Ni Yuping Tu Ying Wang Jiadi Liu |
| author_sort | Quyuan Wang |
| collection | DOAJ |
| description | The rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying economic mechanisms of resource trading. In this study, we examine how task publishers can effectively allocate budgets between computational and data resources during co-training. To address the uncertainty in data acquisition, we introduce Constant Proportion Portfolio Investment approach to assist in the construction of the payoff maximization problem with budget constraints. With the aid of economic tools, we design Swing Gradient Search Algorithm to obtain the optimal investment portfolio strategy, thereby addressing the coupling relationship between the quantities of resource acquisition. We also explore how market dynamics evolve in response to changes in supply and demand. To maintain dynamic market equilibrium, we develop two types of pricing algorithms, one based on stepped price adjustments for selected sellers, and another based on smoothed adjustments for all sellers. Simulation results demonstrate that the proposed strategies and algorithms offer acceptable performance in terms of algorithmic efficiency and strategic effectiveness, while also preserving fundamental economic principles and supporting stable market dynamics. |
| format | Article |
| id | doaj-art-b956ea47a6c64c8fb5532df5cebc82b9 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b956ea47a6c64c8fb5532df5cebc82b92025-08-20T03:55:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032862510.1371/journal.pone.0328625Resource trading strategies with risk selection in collaborative training market.Quyuan WangXinyu NiYuping TuYing WangJiadi LiuThe rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying economic mechanisms of resource trading. In this study, we examine how task publishers can effectively allocate budgets between computational and data resources during co-training. To address the uncertainty in data acquisition, we introduce Constant Proportion Portfolio Investment approach to assist in the construction of the payoff maximization problem with budget constraints. With the aid of economic tools, we design Swing Gradient Search Algorithm to obtain the optimal investment portfolio strategy, thereby addressing the coupling relationship between the quantities of resource acquisition. We also explore how market dynamics evolve in response to changes in supply and demand. To maintain dynamic market equilibrium, we develop two types of pricing algorithms, one based on stepped price adjustments for selected sellers, and another based on smoothed adjustments for all sellers. Simulation results demonstrate that the proposed strategies and algorithms offer acceptable performance in terms of algorithmic efficiency and strategic effectiveness, while also preserving fundamental economic principles and supporting stable market dynamics.https://doi.org/10.1371/journal.pone.0328625 |
| spellingShingle | Quyuan Wang Xinyu Ni Yuping Tu Ying Wang Jiadi Liu Resource trading strategies with risk selection in collaborative training market. PLoS ONE |
| title | Resource trading strategies with risk selection in collaborative training market. |
| title_full | Resource trading strategies with risk selection in collaborative training market. |
| title_fullStr | Resource trading strategies with risk selection in collaborative training market. |
| title_full_unstemmed | Resource trading strategies with risk selection in collaborative training market. |
| title_short | Resource trading strategies with risk selection in collaborative training market. |
| title_sort | resource trading strategies with risk selection in collaborative training market |
| url | https://doi.org/10.1371/journal.pone.0328625 |
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