Efficient Privacy-Preserving Federated Learning for Resource-Constrained Edge Devices
Efficient Privacy-Preserving Federated Learning for Resource-Constrained Edge Devices
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DOI：
10.1109/msn53354.2021.00041
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Jindi Wu;Qi Xia;Qun Li
中科院分区：
文献类型：
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作者：
Jindi Wu;Qi Xia;Qun Li
无处不在的物联网(IoT)设备产生了大量数据，物联网制造商利用这些数据来训练机器学习模型，为用户提供更好的服务。许多物联网数据深度学习系统需要在小型设备上本地执行所有计算，这不适合这些资源受限的设备。这些设备还可以将收集到的所有数据发送到服务器，通过忽略隐私问题进行昂贵的模型训练。为了设计一个高效、安全的深度学习模型训练系统，本文提出了一种边缘联邦学习系统，该系统使用差异化隐私机制来保护敏感信息，并将计算工作从边缘设备转移到边缘服务器，同时考虑到通信的减少。在我们的系统中，大规模的深度学习模型被划分到边缘设备和边缘服务器上，并以分布式的方式进行训练，其中防止所有不可信的组件从训练和推理过程中检索受保护的信息。我们从计算、通信和隐私保护方面对所提出的方法进行了评估。实验结果表明，该方法在保护用户隐私的同时，显著降低了计算和通信代价。
A large volume of data is generated by ubiquitous Internet-of-Things (IoT) devices and utilized to train machine learning models by IoT manufacturers to provide users with better services. Many deep learning systems for IoT data are required to perform all computation locally on small devices, which is not suitable for these resource-constrained devices. The devices can also send all the collected data to a server for costly model training by ignoring privacy concerns. To design an efficient and secure deep learning model training system, in this paper, we propose a federated learning system on the edge using the differential privacy mechanism to protect sensitive information and offload computation work from edge devices to edge servers, with consideration of communication reduction. In our system, a large-scale deep learning model is partitioned onto edge devices and edge servers, and trained in a distributed manner, in which all untrusted components are prevented from retrieving protected information from the training and inference process. We evaluate the proposed approach with respect to computation, communication, and privacy protection. The experiment results show that the proposed approach can preserve users’ privacy while significantly reducing computation and communication costs.