  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.
