While inference is needed at the edge, training is typically done at the cloud. Therefore, data necessary for training a model, as well as the trained model, have to be transmitted back and forth between the edge and the cloud training infrastructure. This creates significant security issues, including the inclusion of a backdoor sent to the user without the user’s knowledge. This article presents an approach where a trained model can still operate as expected, irrespective of the presence of such a backdoor.—Theocharis Theocharides, University of Cyprus —Muhammad Shafique, Technische Universität Wien

中文翻译：

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使用输入模糊和多数投票的神经网络后门抑制

虽然需要在边缘进行推理，但是训练通常是在云上完成的。因此，训练模型和训练模型所需的数据必须在边缘和云训练基础架构之间来回传输。这会造成严重的安全问题，包括在用户不知情的情况下包含发送给用户的后门。本文介绍了一种方法，无论存在这样的后门，经过训练的模型仍然可以按预期运行。—塞浦路斯大学Theocharis Theocharides —维也纳技术大学的穆罕默德·沙菲克（Muhammad Shafique）