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Mobile edge learning (MEL) is a learning paradigm that enables distributed training of machine learning (ML) models over heterogeneous edge devices (e.g., IoT devices). Multiorchestrator MEL refers to the coexistence of multiple learning tasks with different datasets, each of which being governed by an orchestrator to facilitate the distributed training process. In MEL, the training performance deteriorates without the availability of sufficient training data or computing resources. Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task. In this work, we propose an incentive mechanism, where we formulate the orchestrators-learners’ interactions as a 2-round Stackelberg game to motivate the participation of the learners. In the first …