Task-oriented communication aims to extract and transmit task-relevant
information to significantly reduce the communication overhead and transmission
latency. However, the unpredictable distribution shifts between training and
test data, including domain shift and semantic shift, can dramatically
undermine the system performance. In order to tackle these challenges, it is
crucial to ensure that the encoded features can generalize to domain-shifted
data and detect semanticshifted data, while remaining compact for transmission.
In this paper, we propose a novel approach based on the information bottleneck
(IB) principle and invariant risk minimization (IRM) framework. The proposed
method aims to extract compact and informative features that possess high
capability for effective domain-shift generalization and accurate
semantic-shift detection without any knowledge of the test data during
training. Specifically, we propose an invariant feature encoding approach based
on the IB principle and IRM framework for domainshift generalization, which
aims to find the causal relationship between the input data and task result by
minimizing the complexity and domain dependence of the encoded feature.
Furthermore, we enhance the task-oriented communication with the
label-dependent feature encoding approach for semanticshift detection which
achieves joint gains in IB optimization and detection performance. To avoid the
intractable computation of the IB-based objective, we leverage variational
approximation to derive a tractable upper bound for optimization. Extensive
simulation results on image classification tasks demonstrate that the proposed
scheme outperforms state-of-the-art approaches and achieves a better
rate-distortion tradeoff.

在这篇论文中，我们提出了一种基于信息瓶颈原理和不变风险最小化框架的新方法，旨在提取紧凑且信息丰富的特征，以具备对领域转移和语义转移具有强大泛化能力的同时，在传输过程中保持紧凑性。我们在图像分类任务上的大量模拟结果表明，该方案优于当前的最先进方法，并实现了更好的率失真权衡。

应用信息瓶颈解决任务导向通信中的分布偏移问题

Tackling Distribution Shifts in Task-Oriented Communication with  Information Bottleneck

Traditional approaches to semantic communication tasks rely on the knowledge
of the signal-to-noise ratio (SNR) to mitigate channel noise. However, these
methods necessitate training under specific SNR conditions, entailing
considerable time and computational resources. In this paper, we propose GeNet,
a Graph Neural Network (GNN)-based paradigm for semantic communication aimed at
combating noise, thereby facilitating Task-Oriented Communication (TOC). We
propose a novel approach where we first transform the input data image into
graph structures. Then we leverage a GNN-based encoder to extract semantic
information from the source data. This extracted semantic information is then
transmitted through the channel. At the receiver's end, a GNN-based decoder is
utilized to reconstruct the relevant semantic information from the source data
for TOC. Through experimental evaluation, we show GeNet's effectiveness in
anti-noise TOC while decoupling the SNR dependency. We further evaluate GeNet's
performance by varying the number of nodes, revealing its versatility as a new
paradigm for semantic communication. Additionally, we show GeNet's robustness
to geometric transformations by testing it with different rotation angles,
without resorting to data augmentation.

提出了一种基于图神经网络的语义通信范式 GeNet，旨在抵抗噪声，从而促进面向任务的通信（TOC）。通过将输入数据图像转换为图结构，利用基于 GNN 的编码器从源数据中提取语义信息，然后通过信道传输。接收端利用基于 GNN 的解码器从源数据中重构相关的语义信息以实现 TOC。通过实验评估表明，GeNet 在抗噪声的 TOC 中具有有效性，并解耦了 SNR 的依赖性。通过改变节点数量进一步评估了 GeNet 的性能，展示了其作为语义通信的新范式的多用途性。此外，通过在不依赖数据增强的情况下测试具有不同旋转角度的几何变换，证明了 GeNet 的鲁棒性。

GeNet: 基于图神经网络的抗噪任务导向语义通信范式

GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic  Communication Paradigm

We propose a method that allows to develop shared understanding between two
agents for the purpose of performing a task that requires cooperation. Our
method focuses on efficiently establishing successful task-oriented
communication in an open multi-agent system, where the agents do not know
anything about each other and can only communicate via grounded interaction.
The method aims to assist researchers that work on human-machine interaction or
scenarios that require a human-in-the-loop, by defining interaction
restrictions and efficiency metrics. To that end, we point out the challenges
and limitations of such a (diverse) setup, while also restrictions and
requirements which aim to ensure that high task performance truthfully reflects
the extent to which the agents correctly understand each other. Furthermore, we
demonstrate a use-case where our method can be applied for the task of
cooperative query answering. We design the experiments by modifying an
established ontology alignment benchmark. In this example, the agents want to
query each other, while representing different databases, defined in their own
ontologies that contain different and incomplete knowledge. Grounded
interaction here has the form of examples that consists of common instances,
for which the agents are expected to have similar knowledge. Our experiments
demonstrate successful communication establishment under the required
restrictions, and compare different agent policies that aim to solve the task
in an efficient manner.

通过约束交互和定义准确性评估方法，我们提出了一种在开放的多智能体系统中实现任务导向通信的方法，并在此基础上展示了协作查询应答的成功应用。