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Privacy-Preserving Computation Offloading for Parallel Deep Neural Networks Training

机译：Piltacy-Presting计算并行深神经网络训练的卸载

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摘要

Deep neural networks (DNNs) have brought significant performance improvements to various real-life applications. However, a DNN training task commonly requires intensive computing resources and a huge data collection, which makes it hard for personal devices to carry out the entire training, especially for mobile devices. The federated learning concept has eased this situation. However, it is still an open problem for individuals to train their own DNN models at an affordable price. In this article, we propose an alternative DNN training strategy for resource-limited users. With the help of an untrusted server, end users can offload their DNN training tasks to the server in a privacy-preserving manner. To this end, we study the possibility of the separation of a DNN. Then we design a differentially private activation algorithm for end users to ensure the privacy of the offloading after model separation. Furthermore, to meet the rising demand for federated learning, we extend the offloading solution to parallel DNN models training with a secure model weights aggregation scheme for the privacy concern. Experimental results prove the feasibility of computation offloading solutions for DNN models in both solo and parallel modes.
机译：深度神经网络（DNN）对各种现实生活应用带来了显着的性能改进。然而，DNN培训任务通常需要密集的计算资源和巨大的数据收集，这使得个人设备难以执行整个培训，特别是对于移动设备。联邦学习概念缓解了这种情况。但是，个人仍然是一个以实惠的价格训练自己的DNN模型。在本文中，我们为资源有限的用户提出了另一种DNN培训策略。在不受信任的服务器的帮助下，最终用户可以以隐私保留方式将其DNN培训任务卸载到服务器。为此，我们研究了DNN分离的可能性。然后我们设计一个差别的私有激活算法，以确保模型分离后卸载的隐私。此外，为了满足联合学习的不断增长，我们将卸载解决方案扩展到并行DNN模型培训，并为隐私问题的安全模型权重聚合方案扩展。实验结果证明了单独和并联模式下DNN模型计算卸载解决方案的可行性。

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