  Task-oriented communication is an emerging paradigm for next-generation
communication networks, which extracts and transmits task-relevant information,
instead of raw data, for downstream applications. Most existing deep learning
(DL)-based task-oriented communication systems adopt a closed-world scenario,
assuming either the same data distribution for training and testing, or the
system could have access to a large out-of-distribution (OoD) dataset for
retraining. However, in practical open-world scenarios, task-oriented
communication systems need to handle unknown OoD data. Under such
circumstances, the powerful approximation ability of learning methods may force
the task-oriented communication systems to overfit the training data (i.e.,
in-distribution data) and provide overconfident judgments when encountering OoD
data. Based on the information bottleneck (IB) framework, we propose a class
conditional IB (CCIB) approach to address this problem in this paper, supported
by information-theoretical insights. The idea is to extract distinguishable
features from in-distribution data while keeping their compactness and
informativeness. This is achieved by imposing the class conditional latent
prior distribution and enforcing the latent of different classes to be far away
from each other. Simulation results shall demonstrate that the proposed
approach detects OoD data more efficiently than the baselines and
state-of-the-art approaches, without compromising the rate-distortion tradeoff.
