Incentive Mechanism for AI-Based Mobile Applications with Coded Federated Learning
Yuris Mulya Saputra, Diep Ngoc Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz · 2021 IEEE Global Communications Conference (GLOBECOM) · 2021
Federated learning (FL) has emerged as a highly-effective distributed learning framework for various AI-based mobile applications. However, in conventional FL, participating mobile users (MUs) may have limited computing resources to train their local data, which leads to learning quality degradation for the whole FL process. To address this problem, coded FL (codFL) has been recently introduced, allowing MUs to upload part of their coded data to a mobile application provider (MAP) before the learning process. As a result, codFL can not only deal with the MUs' limited computing resources, but also provide more benefits for the MUs to participate in the learning process. Nonetheless, in practice, the MAP and MUs often belong to different parties who unilaterally aim to maximize their individual utility functions. Thus, in this paper, we propose an effective mechanism for the codFL process to incentivize all the participating MUs while improving the learning quality of the MAP. Specifically, we first design a codFL contract optimization problem leveraging a multi-principal one-agent (MPOA) approach in contract theory, under limited computing resources at the MAP and MUs as well as information asymmetry between them. To find the optimal contracts for MUs, we develop an iterative contract algorithm which can produce maximum utilities for all MUs while satisfying all the constraints of the MAP. Numerical results show that our framework can enhance the utilities of MUs up to 113% and system performance in terms of social welfare up to 42% compared with the baseline method.