Edge-cloud collaborative inference empowers resource-limited IoT devices to
support deep learning applications without disclosing their raw data to the
cloud server, thus preserving privacy. Nevertheless, prior research has shown
that collaborative inference still results in the exposure of data and
predictions from edge devices. To enhance the privacy of collaborative
inference, we introduce a defense strategy called PrivaScissors, which is
designed to reduce the mutual information between a model's intermediate
outcomes and the device's data and predictions. We evaluate PrivaScissors's
performance on several datasets in the context of diverse attacks and offer a
theoretical robustness guarantee.

本文介绍了一种名为 PrivaScissors 的防御策略，可通过降低模型中间结果与设备数据、预测之间的互信息来增强协作推理的隐私性，经在多个数据集中进行攻击测试，具备理论上的鲁棒性保证。

PrivaScissors：透过互信息的视角增强协作推断的隐私保护

PrivaScissors: Enhance the Privacy of Collaborative Inference through  the Lens of Mutual Information

In this paper, we study a network slicing problem for edge-cloud orchestrated
vehicular networks, in which the edge and cloud servers are orchestrated to
process computation tasks for reducing network slicing cost while satisfying
the quality of service requirements. We propose a two-stage network slicing
framework, which consists of 1) network planning stage in a large timescale to
perform slice deployment, edge resource provisioning, and cloud resource
provisioning, and 2) network operation stage in a small timescale to perform
resource allocation and task dispatching. Particularly, we formulate the
network slicing problem as a two-timescale stochastic optimization problem to
minimize the network slicing cost. Since the problem is NP-hard due to coupled
network planning and network operation stages, we develop a Two timescAle
netWork Slicing (TAWS) algorithm by collaboratively integrating reinforcement
learning (RL) and optimization methods, which can jointly make network planning
and operation decisions. Specifically, by leveraging the timescale separation
property of decisions, we decouple the problem into a large-timescale network
planning subproblem and a small-timescale network operation subproblem. The
former is solved by an RL method, and the latter is solved by an optimization
method. Simulation results based on real-world vehicle traffic traces show that
the TAWS can effectively reduce the network slicing cost as compared to the
benchmark scheme.

本文提出了一种基于两个时间尺度优化问题的网络切片方法，用于优化边缘云车联网的成本和服务质量，并使用强化学习和优化方法进行网络规划和操作决策。

边缘云管控车联网的经济高效双层网络切片技术

Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks

In edge-cloud collaborative intelligence (CI), an unreliable transmission
channel exists in the information path of the AI model performing the
inference. It is important to be able to simulate the performance of the CI
system across an imperfect channel in order to understand system behavior and
develop appropriate error control strategies. In this paper we present a
simulation framework called DFTS2, which enables researchers to define the
components of the CI system in TensorFlow~2, select a packet-based channel
model with various parameters, and simulate system behavior under various
channel conditions and error/loss control strategies. Using DFTS2, we also
present the most comprehensive study to date of the packet loss concealment
methods for collaborative image classification models.

本文提出了名为 DFTS2 的仿真框架，使得研究人员能够在 TensorFlow2 中对 CI 系统进行组件定义，并选择各种参数的基于数据包的信道模型，以及在各种信道条件和误差 / 丢失控制策略下模拟系统行为。利用 DFTS2，我们还对协作图片分类模型的丢包隐藏方法进行了迄今为止的最全面的研究。

DFTS2：在丢包信道上模拟深度特征传输

DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channels

In many industry scale applications, large and resource consuming machine
learning models reside in powerful cloud servers. At the same time, large
amounts of input data are collected at the edge of cloud. The inference results
are also communicated to users or passed to downstream tasks at the edge. The
edge often consists of a large number of low-power devices. It is a big
challenge to design industry products to support sophisticated deep model
deployment and conduct model inference in an efficient manner so that the model
accuracy remains high and the end-to-end latency is kept low. This paper
describes the techniques and engineering practice behind Auto-Split, an
edge-cloud collaborative prototype of Huawei Cloud. This patented technology is
already validated on selected applications, is on its way for broader
systematic edge-cloud application integration, and is being made available for
public use as an automated pipeline service for end-to-end cloud-edge
collaborative intelligence deployment. To the best of our knowledge, there is
no existing industry product that provides the capability of Deep Neural
Network (DNN) splitting.

探讨了华为云的 Auto-Split 系统采用的边缘 - 云协作技术，实现了高效的深度学习模型部署和推理，为行业级应用提供新的解决方案。