Towards Edge General Intelligence: Knowledge Distillation for Mobile Agentic AI
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911284778237952 |
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| author | Wu, Yuxuan Ma, Linghan Zhang, Ruichen Liu, Yinqiu Niyato, Dusit Tang, Shunpu Xiong, Zehui Han, Zhu Yang, Zhaohui Huang, Kaibin Zhang, Zhaoyang Wong, Kai-Kit |
| author_facet | Wu, Yuxuan Ma, Linghan Zhang, Ruichen Liu, Yinqiu Niyato, Dusit Tang, Shunpu Xiong, Zehui Han, Zhu Yang, Zhaohui Huang, Kaibin Zhang, Zhaoyang Wong, Kai-Kit |
| contents | Edge General Intelligence (EGI) represents a paradigm shift in mobile edge computing, where intelligent agents operate autonomously in dynamic, resource-constrained environments. However, the deployment of advanced agentic AI models on mobile and edge devices faces significant challenges due to limited computation, energy, and storage resources. To address these constraints, this survey investigates the integration of Knowledge Distillation (KD) into EGI, positioning KD as a key enabler for efficient, communication-aware, and scalable intelligence at the wireless edge. In particular, we emphasize KD techniques specifically designed for wireless communication and mobile networking, such as channel-aware self-distillation, cross-model Channel State Information (CSI) feedback distillation, and robust modulation/classification distillation. Furthermore, we review novel architectures natively suited for KD and edge deployment, such as Mamba, RWKV (Receptance, Weight, Key, Value) and Cross-Architecture distillation, which enhance generalization capabilities. Subsequently, we examine diverse applications in which KD-driven architectures enable EGI across vision, speech, and multimodal tasks. Finally, we highlight the key challenges and future directions for KD in EGI. This survey aims to provide a comprehensive reference for researchers exploring KD-driven frameworks for mobile agentic AI in the era of EGI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Edge General Intelligence: Knowledge Distillation for Mobile Agentic AI Wu, Yuxuan Ma, Linghan Zhang, Ruichen Liu, Yinqiu Niyato, Dusit Tang, Shunpu Xiong, Zehui Han, Zhu Yang, Zhaohui Huang, Kaibin Zhang, Zhaoyang Wong, Kai-Kit Information Theory Signal Processing Edge General Intelligence (EGI) represents a paradigm shift in mobile edge computing, where intelligent agents operate autonomously in dynamic, resource-constrained environments. However, the deployment of advanced agentic AI models on mobile and edge devices faces significant challenges due to limited computation, energy, and storage resources. To address these constraints, this survey investigates the integration of Knowledge Distillation (KD) into EGI, positioning KD as a key enabler for efficient, communication-aware, and scalable intelligence at the wireless edge. In particular, we emphasize KD techniques specifically designed for wireless communication and mobile networking, such as channel-aware self-distillation, cross-model Channel State Information (CSI) feedback distillation, and robust modulation/classification distillation. Furthermore, we review novel architectures natively suited for KD and edge deployment, such as Mamba, RWKV (Receptance, Weight, Key, Value) and Cross-Architecture distillation, which enhance generalization capabilities. Subsequently, we examine diverse applications in which KD-driven architectures enable EGI across vision, speech, and multimodal tasks. Finally, we highlight the key challenges and future directions for KD in EGI. This survey aims to provide a comprehensive reference for researchers exploring KD-driven frameworks for mobile agentic AI in the era of EGI. |
| title | Towards Edge General Intelligence: Knowledge Distillation for Mobile Agentic AI |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2511.19947 |