Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?
The deployment of data-driven models in 6G wireless networks is increasingly challenged by frequent distribution shifts that degrade performance over time. Unsupervised Domain Adaptation (UDA) offers an alternative approach by adapting the trained model to a shifted domain without requiring labels. However, UDA pipelines are often more complex than single-task training due to additional modules and optimization procedures, raising a practical question: do the benefits of adaptation come at a higher energy cost, and how does this trade-off compare to retraining when labeling effort is also considered? In this work, we investigate the energy consumption of UDA and compare it to single task....
| Authors | Illyyne Saffar, Aurélie Boisbunon, Shruti Bothe |
|---|---|
| Primary category | stat.ML |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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