  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.
