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.

为了克服 6G 移动网络中多设备上同时下载不同和高维模型的通信瓶颈，我们提出了模型广播和装配（MBA）框架，通过参数广播减少通信开销和下载延迟，同时提供设备的模型性能保证。与传统模型下载相比，所提出的 MBA 框架在下载延迟方面取得了显著的减少。

通过可重用的知识广播实现高效多用户 AI 下载

Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting

The sixth-generation (6G) mobile networks are expected to feature the
ubiquitous deployment of machine learning and AI algorithms at the network
edge. With rapid advancements in edge AI, the time has come to realize
intelligence downloading onto edge devices (e.g., smartphones and sensors). To
materialize this version, we propose a novel technology in this article, called
in-situ model downloading, that aims to achieve transparent and real-time
replacement of on-device AI models by downloading from an AI library in the
network. Its distinctive feature is the adaptation of downloading to
time-varying situations (e.g., application, location, and time), devices'
heterogeneous storage-and-computing capacities, and channel states. A key
component of the presented framework is a set of techniques that dynamically
compress a downloaded model at the depth-level, parameter-level, or bit-level
to support adaptive model downloading. We further propose a virtualized 6G
network architecture customized for deploying in-situ model downloading with
the key feature of a three-tier (edge, local, and central) AI library.
Furthermore, experiments are conducted to quantify 6G connectivity requirements
and research opportunities pertaining to the proposed technology are discussed.

通过应用 in-situ 模型下载技术，可以在 6G 移动网络中实现发射设备（如智能手机和传感器）的智能化。该技术可提供透明和实时的模型替换，可以根据时间变化的情况（如应用，位置和时间），设备异构存储和计算能力以及信道状态进行自适应模型下载。一项关键技术是在深度级别，参数级别或比特级别上动态压缩下载模型，以支持自适应模型下载。为该框架量身定制了三层（边缘、本地和中心）AI 库的虚拟 6G 网络架构。此外，还进行了实验来量化 6G 连接要求，并讨论了有关所提出技术的研究机会。