Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models
Mobile systems increasingly rely on heterogeneous learning-enabled wireless functions, for which separate taskspecific models incur redundant training and model-management overhead. Wireless foundation models (WFMs) enable these functions to share a pretrained backbone, but existing adaptation either updates the backbone per task or relies on an inflexible final-layer representation. We observe that intermediate WFM layers exhibit distinct depth-dependent correlation structures. Based on this observation, we propose a Routing Adapter for Feature Composition (RAFC), which summarizes selected hidden states into compact descriptors, generates task- and sampledependent routing weights, and co...
| Authors | Yuxuan Shi, Tingting Yang, Li Sun et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 June 2026 |
| Last updated at source | 25 August 2026 |
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