Title: Joint Channel and Semantic-aware Grouping for Effective Collaborative Edge Inference Show Chinese title

Title: 联合信道和语义感知分组用于有效的协作边缘推理

Abstract: We focus on collaborative edge inference over wireless, which enables multiple devices to cooperate to improve inference performance in the presence of corrupted data. Exploiting a key-query mechanism for selective information exchange (or, group formation for collaboration), we recall the effect of wireless channel impairments in feature communication. We argue and show that a disjoint approach, which only considers either the semantic relevance or channel state between devices, performs poorly, especially in harsh propagation conditions. Based on these findings, we propose a joint approach that takes into account semantic information relevance and channel states when grouping devices for collaboration, by making the general attention weights dependent of the channel information. Numerical simulations show the superiority of the joint approach against local inference on corrupted data, as well as compared to collaborative inference with disjoint decisions that either consider application or physical layer parameters when forming groups. Show Chinese abstract

Abstract: 我们专注于无线环境下的协同边缘推理，这使得多个设备能够协作以在存在损坏数据的情况下提高推理性能。 利用关键查询机制进行选择性信息交换（或，协作的组形成），我们回顾了无线信道退化在特征通信中的影响。 我们认为并证明，仅考虑设备之间语义相关性或信道状态的分离方法，在恶劣传播条件下表现较差。 基于这些发现，我们提出了一种联合方法，在对设备进行分组协作时，同时考虑语义信息相关性和信道状态，通过使通用注意力权重依赖于信道信息。 数值模拟显示，与在损坏数据上的本地推理相比，以及与在分组时仅考虑应用层或物理层参数的协作推理相比，联合方法具有优越性。