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FedMEC: Improving Efficiency of Differentially Private Federated Learning via Mobile Edge Computing

机译：FEDMEC：通过移动边缘计算提高差异私有联合学习的效率

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摘要

Federated learning is a recently proposed paradigm that presents significant advantages in privacy-preserving machine learning services. It enables the deep learning applications on mobile devices, where a deep neural network (DNN) is trained in a decentralized manner among thousands of edge clients. However, directly apply the federated learning algorithm to the mobile edge computing environment will incur unacceptable computation costs in mobile edge devices. Moreover, among the training process, frequent model parameters exchanging between participants and the central server will increase the leakage possibility of the users' sensitive training data. Aiming at reducing the heavy computation cost of DNN training on edge devices while providing strong privacy guarantees, we propose a mobile edge computing enabled federated learning framework, called FedMEC, which integrating model partition technique and differential privacy simultaneously. In FedMEC, the most complex computations can be outsourced to the edge servers by splitting a DNN model into two parts. Furthermore, we apply the differentially private data perturbation method to prevent the privacy leakage from the local model parameters, in which the updates from an edge device to the edge server is perturbed by the Laplace noise. To validate the proposed FedMEC, we conduct a series of experiments on an image classification task under the settings of federated learning. The results demonstrate the effectiveness and practicality of our FedMEC scheme.
机译：联邦学习是最近提出的范式，在隐私保存机学习服务中具有显着的优势。它使移动设备上的深度学习应用能够以数千个边缘客户端以分散的方式训练深度神经网络（DNN）。但是，直接将联合学习算法应用于移动边缘计算环境将在移动边缘设备中产生不可接受的计算成本。此外，在培训过程中，参与者和中央服务器之间交换的频繁模型参数将增加用户敏感培训数据的泄漏可能性。旨在降低边缘设备的DNN训练的繁重计算成本，同时提供强大的隐私保障，我们提出了一种移动边缘计算，使得能够同时集成模型分区技术和差异隐私的联邦学习框架。在FEDMEC中，最复杂的计算可以通过将DNN模型分为两个部分来将最复杂的计算外包给边缘服务器。此外，我们应用差别私有数据扰动方法，以防止来自本地模型参数的隐私泄漏，其中来自边缘设备到边缘服务器的更新被拉普拉斯噪声扰乱。为了验证拟议的FedMec，我们在联合学习的环境下对图像分类任务进行了一系列实验。结果展示了我们的补助金计划的有效性和实用性。

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