Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective
Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective
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DOI：
10.1109/tvt.2022.3161099
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发表时间：
2022-02
影响因子：
6.8
通讯作者：
Qin Hu;Feng Li;X. Zou;Yinhao Xiao
中科院分区：
文献类型：
--
作者：
Qin Hu;Feng Li;X. Zou;Yinhao Xiao
一种新兴的计算范式，称为联合边缘学习（FEL），可以在网络边缘实现智能计算，并具有保护边缘设备数据隐私的功能。由于资源有限，FEL 实现高执行性能成为一个巨大的挑战。大多数最先进的技术集中于从系统操作过程的角度增强 FEL，在 FEL 系统的组成步骤中很少采取预防措施。尽管最近的一些研究认识到 FEL 形成的重要性并提出了以服务器为中心的设备选择方案，但数据大小的影响在很大程度上被忽视了。在本文中，我们利用博弈论来描述边缘设备在考虑到本地数据集的异构大小时是否参与 FEL 的决策困境。为了实现个体和全局优化，采用服务器来解决参与困境，这需要对设备本地数据集进行准确的信息收集。因此，我们利用机制设计来实现真实的信息征求。借助相关均衡，我们从全局角度推导出设备的决策策略，从而实现FEL的长期稳定性和有效性。出于可扩展性的考虑，我们将基本解的计算复杂度优化至多项式级别。最后，基于真实数据和合成数据进行了广泛的实验来评估我们提出的机制，实验结果证明了性能优势。
An emerging computational paradigm, named federated edge learning (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding whether to participate in FEL or not given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices’ local datasets. Hence, we utilize mechanism design to enable truthful information solicitation. With the help of correlated equilibrium, we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages.