Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning

Abstract

Federated learning (FL) can protect data privacy but has difficulties in motivating user equipment (UE) to engage in task training. This paper proposes a Bertrand-game based framework to address the incentive problem, where a model owner (MO) issues an FL task and the employed UEs help train the model by using their local data. Specially, we consider the impact of time-varying task load and channel quality on UE's motivation to engage in the FL task. We adopt the finitestate discrete-time Markov chain (FSDT-MC) to predict these parameters during the FL task. Depending on the performance metrics set by the MO and the estimated energy cost of the FL task, each UE seeks to maximize its profit. We obtain the Nash equilibrium (NE) of the game in closed form, and develop a distributed iterative algorithm to find it. Finally, simulation result verifies the effectiveness of the proposed approach.

Spectrum and Computing Resource Management for Federated Learning in Distributed Industrial IoT

“…The above issues have been partially addressed in existing work, such as the client selection problem studied in [2] [3] [4], the multi-objective trade-off problem considered in [4] [5], and the incentive mechanisms designed in [2] [5] [6] [7]. However, the proposed methods lack a holistic perspective Han Zhang and Guopeng Zhang are with the School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China (e-mail: hanzhangl@cumt.edu.cn; gpzhang@cumt.edu.cn).…”