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This paper explores opportunities and challenges of task (goal)-oriented andsemantic communications for next-generation (NextG) communication networksthrough the integration of multi-task learning. This approach employs deepneural networks representing a dedicated encoder at the transmitter andmultiple task-specific decoders at the receiver, collectively trained to handlediverse tasks including semantic information preservation, source inputreconstruction, and integrated sensing and communications. To extend theapplicability from point-to-point links to multi-receiver settings, we envisionthe deployment of decoders at various receivers, where decentralized learningaddresses the challenges of communication load and privacy concerns, leveragingfederated learning techniques that distribute model updates acrossdecentralized nodes. However, the efficacy of this approach is contingent onthe robustness of the employed deep learning models. We scrutinize potentialvulnerabilities stemming from adversarial attacks during both training andtesting phases. These attacks aim to manipulate both the inputs at the encoderat the transmitter and the signals received over the air on the receiver side,highlighting the importance of fortifying semantic communications againstpotential multi-domain exploits. Overall, the joint and robust design oftask-oriented communications, semantic communications, and integrated sensingand communications in a multi-task learning framework emerges as the keyenabler for context-aware, resource-efficient, and secure communicationsultimately needed in NextG network systems.

Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing