For the 6G mobile networks, in-situ model downloading has emerged as an important use case to enable real-time adaptive artificial intelligence on edge devices. However, the simultaneous downloading of diverse and high-dimensional models to multiple devices over wireless links presents a significant communication bottleneck. To overcome the bottleneck, we propose the framework of model broadcasting and assembling (MBA), which represents the first attempt on leveraging reusable knowledge, referring to shared parameters among tasks, to enable parameter broadcasting to reduce communication overhead. The MBA framework comprises two key components. The first, the MBA protocol, defines the system operations including parameter selection from a model library, power control for broadcasting, and model assembling at devices. The second component is the joint design of parameter-selection-and-power-control (PS-PC), which provides guarantees on devices' model performance and minimizes the downloading latency. The corresponding optimization problem is simplified by decomposition into the sequential PS and PC sub-problems without compromising its optimality. The PS sub-problem is solved efficiently by designing two efficient algorithms. On one hand, the low-complexity algorithm of greedy parameter selection features the construction of candidate model sets and a selection metric, both of which are designed under the criterion of maximum reusable knowledge among tasks. On the other hand, the optimal tree-search algorithm gains its efficiency via the proposed construction of a compact binary tree pruned using model architecture constraints and an intelligent branch-and-bound search. Given optimal PS, the optimal PC policy is derived in closed form. Extensive experiments demonstrate the substantial reduction in downloading latency achieved by the proposed MBA compared to traditional model downloading.

翻译：对于6G移动网络，原位模型下载已成为实现边缘设备实时自适应人工智能的重要用例。然而，通过无线链路向多个设备同时下载多样化高维模型带来了显著的通信瓶颈。为突破该瓶颈，我们提出模型广播与组装（MBA）框架，这是首次尝试利用可复用知识（即任务间共享参数）实现参数广播以降低通信开销。MBA框架包含两个关键组件：第一，MBA协议定义了系统操作流程，包括从模型库中选择参数、广播功率控制以及设备端模型组装；第二，参数选择-功率控制（PS-PC）联合设计，能够保障设备模型性能并最小化下载延迟。通过将相应优化问题分解为顺序的PS和PC子问题（且不损失最优性），可实现求解简化。PS子问题通过设计两种高效算法得以有效解决：一方面，低复杂度的贪婪参数选择算法通过构建候选模型集和选择度量（两者均基于任务间最大可复用知识准则设计）实现高效性；另一方面，最优树搜索算法通过提出紧凑二叉树结构（利用模型架构约束剪枝并结合智能分支定界搜索）提升效率。在给定最优PS的前提下，可推导出最优PC策略的闭式解。大量实验表明，与传统模型下载相比，所提出的MBA框架能够显著降低下载延迟。