Towards Neural Architecture Search for Transfer Learning in 6G Networks
<|reference_start|>Towards Neural Architecture Search for Transfer Learning in 6G Networks: 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.<|reference_end|>
@article{orucu2024towards,
    title={Towards Neural Architecture Search for Transfer Learning in 6G Networks},
    author={Adam Orucu, Farnaz Moradi, Masoumeh Ebrahimi and Andreas Johnsson},
    journal={arXiv preprint arXiv:2406.02333},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02333},
    primaryClass={cs.NI cs.AI cs.LG}
}