  Mobile Edge Learning (MEL) is a learning paradigm that enables distributed
training of Machine Learning models over heterogeneous edge devices (e.g., IoT
devices). Multi-orchestrator 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 round, the learners decide which learning task to get engaged in, and
then in the second round, the training parameters and the amount of data for
training in case of participation such that their utility is maximized. We then
study the training round analytically and derive the learners' optimal
strategy. Finally, numerical experiments have been conducted to evaluate the
performance of the proposed incentive mechanism.
