  This paper explores opportunities and challenges of task (goal)-oriented and
semantic communications for next-generation (NextG) communication networks
through the integration of multi-task learning. This approach employs deep
neural networks representing a dedicated encoder at the transmitter and
multiple task-specific decoders at the receiver, collectively trained to handle
diverse tasks including semantic information preservation, source input
reconstruction, and integrated sensing and communications. To extend the
applicability from point-to-point links to multi-receiver settings, we envision
the deployment of decoders at various receivers, where decentralized learning
addresses the challenges of communication load and privacy concerns, leveraging
federated learning techniques that distribute model updates across
decentralized nodes. However, the efficacy of this approach is contingent on
the robustness of the employed deep learning models. We scrutinize potential
vulnerabilities stemming from adversarial attacks during both training and
testing phases. These attacks aim to manipulate both the inputs at the encoder
at the transmitter and the signals received over the air on the receiver side,
highlighting the importance of fortifying semantic communications against
potential multi-domain exploits. Overall, the joint and robust design of
task-oriented communications, semantic communications, and integrated sensing
and communications in a multi-task learning framework emerges as the key
enabler for context-aware, resource-efficient, and secure communications
ultimately needed in NextG network systems.
