  This paper studies task-oriented, otherwise known as goal-oriented,
communications, in a setting where a transmitter communicates with multiple
receivers, each with its own task to complete on a dataset, e.g., images,
available at the transmitter. A multi-task deep learning approach that involves
training a common encoder at the transmitter and individual decoders at the
receivers is presented for joint optimization of completing multiple tasks and
communicating with multiple receivers. By providing efficient resource
allocation at the edge of 6G networks, the proposed approach allows the
communications system to adapt to varying channel conditions and achieves
task-specific objectives while minimizing transmission overhead. Joint training
of the encoder and decoders using multi-task learning captures shared
information across tasks and optimizes the communication process accordingly.
By leveraging the broadcast nature of wireless communications, multi-receiver
task-oriented communications (MTOC) reduces the number of transmissions
required to complete tasks at different receivers. Performance evaluation
conducted on the MNIST, Fashion MNIST, and CIFAR-10 datasets (with image
classification considered for different tasks) demonstrates the effectiveness
of MTOC in terms of classification accuracy and resource utilization compared
to single-task-oriented communication systems.
