Online Incentive Mechanism Designs for Asynchronous Federated Learning in Edge Computing.

In this paper, we consider incentive mechanism designs in asynchronous federated learning (FL) systems. With the consideration of unique characteristics inherent in asynchronous FL, such as dynamic participating and multi-minded IoT nodes such as mobile users, requirements of model training (i.e., training accuracy and convergence time), and limited uplink bandwidth, we formulate considered system as an online incentive mechanism design problem, where each mobile user is not only a buyer for communication resource, but also a seller for computation service. To address the challenges involved in the design, we first derive the relationship between the number of participants and the global training accuracy in asynchronous FL. Then, based on that, we propose a novel mechanism, called online incentive mechanism for asynchronous FL (OIMAF). To the best of our knowledge, this is the first work to design incentive mechanisms for asynchronous FL. Furthermore, in order to obtain a more robust mechanism, an improved online mechanism, called two-shot based online incentive mechanism (TOIM), is proposed by using OIMAF as a building block. Theoretical analyses show that our proposed online incentive mechanisms can guarantee individual rationality, truthfulness, a sound performance, and solution feasibilities. We further conduct comprehensive simulations to validate the effectiveness of our proposed mechanisms.