Techniques and apparatuses are described for deep neural network (DNN) processing for a user equipment-coordination set (UECS). A network entity selects (910) an end-to-end (E2E) machine-learning (ML) configuration that forms an E2E DNN for processing UECS communications. The network entity directs (915) each device of multiple devices participating in an UECS to form, using at least a portion of the E2E ML configuration, a respective sub-DNN of the E2E DNN that transfers the UECS communications through the E2E communication, where the multiple devices include at least one base station, a coordinating user equipment (UE), and at least one additional UE. The network entity receives (940) feedback associated with the UECS communications and identifies (945) an adjustment to the E2E ML configuration. The network entity then directs at least some of the multiple devices participating in an UECS to update the respective sub-DNN of the E2E DNN based on the adjustment.