  Federated Learning (FL) has increasingly been recognized as an innovative and
secure distributed model training paradigm, aiming to coordinate multiple edge
clients to collaboratively train a shared model without uploading their private
datasets. The challenge of encouraging mobile edge devices to participate
zealously in FL model training procedures, while mitigating the privacy leakage
risks during wireless transmission, remains comparatively unexplored so far. In
this paper, we propose a novel approach, named QI-DPFL (Quality-Aware and
Incentive-Boosted Federated Learning with Differential Privacy), to address the
aforementioned intractable issue. To select clients with high-quality datasets,
we first propose a quality-aware client selection mechanism based on the Earth
Mover's Distance (EMD) metric. Furthermore, to attract high-quality data
contributors, we design an incentive-boosted mechanism that constructs the
interactions between the central server and the selected clients as a two-stage
Stackelberg game, where the central server designs the time-dependent reward to
minimize its cost by considering the trade-off between accuracy loss and total
reward allocated, and each selected client decides the privacy budget to
maximize its utility. The Nash Equilibrium of the Stackelberg game is derived
to find the optimal solution in each global iteration. The extensive
experimental results on different real-world datasets demonstrate the
effectiveness of our proposed FL framework, by realizing the goal of privacy
protection and incentive compatibility.
