Fast Context Adaptation in Cost-Aware Continual Learning
In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However, the increased complexity of 5G and Beyond networks requires correspondingly more complex...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10495063/ |
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| author | Seyyidahmed Lahmer Federico Mason Federico Chiariotti Andrea Zanella |
| author_facet | Seyyidahmed Lahmer Federico Mason Federico Chiariotti Andrea Zanella |
| author_sort | Seyyidahmed Lahmer |
| collection | DOAJ |
| description | In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However, the increased complexity of 5G and Beyond networks requires correspondingly more complex learning agents and the learning process itself might end up competing with users for communication and computational resources. This creates friction: on the one hand, the learning process needs resources to quickly converge to an effective strategy; on the other hand, the learning process needs to be efficient, i.e., take as few resources as possible from the user’s data plane, so as not to throttle users’ Quality of Service (QoS). In this paper, we investigate this trade-off, which we refer to as cost of learning, and propose a dynamic strategy to balance the resources assigned to the data plane and those reserved for learning. With the proposed approach, a learning agent can quickly converge to an efficient resource allocation strategy and adapt to changes in the environment as for the Continual Learning (CL) paradigm, while minimizing the impact on the users’ QoS. Simulation results show that the proposed method outperforms static allocation methods with minimal learning overhead, almost reaching the performance of an ideal out-of-band CL solution. |
| format | Article |
| id | doaj-art-325cfbaa40f84aa897d5cb38dd4290c8 |
| institution | OA Journals |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-325cfbaa40f84aa897d5cb38dd4290c82025-08-20T02:05:01ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-01247949410.1109/TMLCN.2024.338664710495063Fast Context Adaptation in Cost-Aware Continual LearningSeyyidahmed Lahmer0Federico Mason1https://orcid.org/0000-0001-5681-1695Federico Chiariotti2https://orcid.org/0000-0002-7915-7275Andrea Zanella3https://orcid.org/0000-0003-3671-5190Department of Information Engineering, University of Padua, Padua, ItalyDepartment of Information Engineering, University of Padua, Padua, ItalyDepartment of Information Engineering, University of Padua, Padua, ItalyDepartment of Information Engineering, University of Padua, Padua, ItalyIn the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However, the increased complexity of 5G and Beyond networks requires correspondingly more complex learning agents and the learning process itself might end up competing with users for communication and computational resources. This creates friction: on the one hand, the learning process needs resources to quickly converge to an effective strategy; on the other hand, the learning process needs to be efficient, i.e., take as few resources as possible from the user’s data plane, so as not to throttle users’ Quality of Service (QoS). In this paper, we investigate this trade-off, which we refer to as cost of learning, and propose a dynamic strategy to balance the resources assigned to the data plane and those reserved for learning. With the proposed approach, a learning agent can quickly converge to an efficient resource allocation strategy and adapt to changes in the environment as for the Continual Learning (CL) paradigm, while minimizing the impact on the users’ QoS. Simulation results show that the proposed method outperforms static allocation methods with minimal learning overhead, almost reaching the performance of an ideal out-of-band CL solution.https://ieeexplore.ieee.org/document/10495063/Resource allocationreinforcement learningcost of learningcontinual learningmeta-learningmobile edge computing |
| spellingShingle | Seyyidahmed Lahmer Federico Mason Federico Chiariotti Andrea Zanella Fast Context Adaptation in Cost-Aware Continual Learning IEEE Transactions on Machine Learning in Communications and Networking Resource allocation reinforcement learning cost of learning continual learning meta-learning mobile edge computing |
| title | Fast Context Adaptation in Cost-Aware Continual Learning |
| title_full | Fast Context Adaptation in Cost-Aware Continual Learning |
| title_fullStr | Fast Context Adaptation in Cost-Aware Continual Learning |
| title_full_unstemmed | Fast Context Adaptation in Cost-Aware Continual Learning |
| title_short | Fast Context Adaptation in Cost-Aware Continual Learning |
| title_sort | fast context adaptation in cost aware continual learning |
| topic | Resource allocation reinforcement learning cost of learning continual learning meta-learning mobile edge computing |
| url | https://ieeexplore.ieee.org/document/10495063/ |
| work_keys_str_mv | AT seyyidahmedlahmer fastcontextadaptationincostawarecontinuallearning AT federicomason fastcontextadaptationincostawarecontinuallearning AT federicochiariotti fastcontextadaptationincostawarecontinuallearning AT andreazanella fastcontextadaptationincostawarecontinuallearning |