Towards Neural Architecture Search for Transfer Learning in 6G Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929373305634816 |
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| author | Orucu, Adam Moradi, Farnaz Ebrahimi, Masoumeh Johnsson, Andreas |
| author_facet | Orucu, Adam Moradi, Farnaz Ebrahimi, Masoumeh Johnsson, Andreas |
| contents | The future 6G network is envisioned to be AI-native, and as such, ML models will be pervasive in support of optimizing performance, reducing energy consumption, and in coping with increasing complexity and heterogeneity. A key challenge is automating the process of finding optimal model architectures satisfying stringent requirements stemming from varying tasks, dynamicity and available resources in the infrastructure and deployment positions. In this paper, we describe and review the state-of-the-art in Neural Architecture Search and Transfer Learning and their applicability in networking. Further, we identify open research challenges and set directions with a specific focus on three main requirements with elements unique to the future network, namely combining NAS and TL, multi-objective search, and tabular data. Finally, we outline and discuss both near-term and long-term work ahead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02333 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Neural Architecture Search for Transfer Learning in 6G Networks Orucu, Adam Moradi, Farnaz Ebrahimi, Masoumeh Johnsson, Andreas Networking and Internet Architecture Artificial Intelligence Machine Learning The future 6G network is envisioned to be AI-native, and as such, ML models will be pervasive in support of optimizing performance, reducing energy consumption, and in coping with increasing complexity and heterogeneity. A key challenge is automating the process of finding optimal model architectures satisfying stringent requirements stemming from varying tasks, dynamicity and available resources in the infrastructure and deployment positions. In this paper, we describe and review the state-of-the-art in Neural Architecture Search and Transfer Learning and their applicability in networking. Further, we identify open research challenges and set directions with a specific focus on three main requirements with elements unique to the future network, namely combining NAS and TL, multi-objective search, and tabular data. Finally, we outline and discuss both near-term and long-term work ahead. |
| title | Towards Neural Architecture Search for Transfer Learning in 6G Networks |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.02333 |