User experience on mobile devices is constrained by limited battery capacity
and processing power, but 6G technology advancements are diving rapidly into
mobile technical evolution. Mobile edge computing (MEC) offers a solution,
offloading computationally intensive tasks to edge cloud servers, reducing
battery drain compared to local processing. The upcoming integrated sensing and
communication in mobile communication may improve the trajectory prediction and
processing delays. This study proposes a greedy resource allocation
optimization strategy for multi-user networks to minimize aggregate energy
usage. Numerical results show potential improvement at 33\% for every 1000
iteration. Addressing prediction model division and velocity accuracy issues is
crucial for better results. A plan for further improvement and achieving
objectives is outlined for the upcoming work phase.

移动设备上的用户体验受到有限的电池容量和处理能力的限制，但 6G 技术的进步正在迅速促使移动技术的演进。移动边缘计算（MEC）提供了一种解决方案，将计算密集型任务卸载到边缘云服务器，与本地处理相比减少了电池消耗。即将到来的移动通信的集成感知与通信可能改善轨迹预测和处理延迟。本研究提出了一种贪婪资源分配优化策略，用于多用户网络以最小化聚合能量使用。数值结果显示，每 1000 次迭代，潜在改进可达 33％。解决预测模型划分和速度精确性问题对于获得更好的结果至关重要。为即将到来的工作阶段详述了进一步改进和实现目标的计划。

基于 Koopman 轨迹模型的 ISAC 启用的物联网系统中的高移动性范式和计算卸载

Koopman based trajectory model and computation offloading for high  mobility paradigm in ISAC enabled IoT system

Split Federated Learning (SFL) has recently emerged as a promising
distributed learning technology, leveraging the strengths of both federated
learning and split learning. It emphasizes the advantages of rapid convergence
while addressing privacy concerns. As a result, this innovation has received
significant attention from both industry and academia. However, since the model
is split at a specific layer, known as a cut layer, into both client-side and
server-side models for the SFL, the choice of the cut layer in SFL can have a
substantial impact on the energy consumption of clients and their privacy, as
it influences the training burden and the output of the client-side models.
Moreover, the design challenge of determining the cut layer is highly
intricate, primarily due to the inherent heterogeneity in the computing and
networking capabilities of clients. In this article, we provide a comprehensive
overview of the SFL process and conduct a thorough analysis of energy
consumption and privacy. This analysis takes into account the influence of
various system parameters on the cut layer selection strategy. Additionally, we
provide an illustrative example of the cut layer selection, aiming to minimize
the risk of clients from reconstructing the raw data at the server while
sustaining energy consumption within the required energy budget, which involve
trade-offs. Finally, we address open challenges in this field including their
applications to 6G technology. These directions represent promising avenues for
future research and development.

通过综合分析能耗和隐私影响，提供选择切割层的策略并应用于 6G 技术中，为分离式联邦学习（SFL）过程提供全面概述和发展方向。