QI-DPFL：结合差分隐私的质量感知与激励增强联邦学习
QI-DPFL: Quality-Aware and Incentive-Boosted Federated Learning with Differential Privacy
AI总结：
针对联邦学习中边缘设备参与激励不足与传输隐私泄露的问题，提出QI-DPFL框架，通过基于EMD的质量感知客户端筛选和两阶段Stackelberg博弈激励机制，实现隐私保护与激励兼容，经真实数据集验证有效。
摘要：
联邦学习（Federated Learning, FL）作为一种创新且安全的分布式模型训练范式日益受到认可，其目标是协调多个边缘客户端协同训练共享模型，且无需上传各自的私有数据集。当前，如何激励移动边缘设备积极参与FL模型训练流程，同时降低无线传输过程中的隐私泄露风险，这一挑战仍相对缺乏深入研究。本文提出了一种名为QI-DPFL（结合差分隐私的质量感知与激励增强联邦学习）的新方法，以解决上述棘手问题。为筛选出拥有高质量数据集的客户端，我们首先提出了一种基于推土机距离（Earth Mover's Distance, EMD）指标的质量感知客户端选择机制。此外，为吸引高质量数据贡献者，我们设计了一种激励增强机制，将中心服务器与被选中客户端之间的交互构建为两阶段Stackelberg博弈：中心服务器设计随时间变化的奖励，通过权衡精度损失与分配的总奖励来最小化自身成本；每个被选中的客户端则决定隐私预算以最大化自身效用。我们推导了该Stackelberg博弈的纳什均衡，以在每次全局迭代中找到最优解。在不同真实世界数据集上的大量实验结果表明，我们提出的FL框架在实现隐私保护和激励兼容目标的同时，具备良好的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
