SplitOut：通过离群点检测实现分割学习中开箱即用的训练劫持检测
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection
AI总结：
本文提出 SplitOut，利用开箱即用的离群点检测方法检测分割学习中的训练劫持攻击，在适度客户端算力假设下实现几乎零假阳性率，比以往基于启发式的方法更简单可靠。
摘要：
分割学习通过将神经网络拆分，使客户端（数据持有者）计算前几层并仅将中间输出共享给计算密集型的中央服务器，从而实现高效且具有隐私意识的深度神经网络训练。该范式引入了一种新的攻击媒介，其中服务器对客户端模型学习的内容拥有完全控制权，这已被利用来推断客户端的私有数据并在客户端模型中植入后门。尽管先前的工作表明客户端能够成功检测此类训练劫持攻击，但所提出的方法依赖启发式规则，需要调整大量超参数，并且未能充分利用客户端的能力。在本工作中，我们表明，在关于客户端计算能力的适度假设下，一种开箱即用的离群点检测方法可用于检测现有的训练劫持攻击，且假阳性率几乎为零。我们通过在不同任务上的实验得出结论：我们命名为 SplitOut 的方法因其简单性，与早期的检测方法相比是一种更可行且更可靠的替代方案。
英文摘要：
Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and only share the intermediate output with the central compute-heavy server. This paradigm introduces a new attack medium in which the server has full control over what the client models learn, which has already been exploited to infer the private data of clients and to implement backdoors in the client models. Although previous work has shown that clients can successfully detect such training-hijacking attacks, the proposed methods rely on heuristics, require tuning of many hyperparameters, and do not fully utilize the clients' capabilities. In this work, we show that given modest assumptions regarding the clients' compute capabilities, an out-of-the-box outlier detection method can be used to detect existing training-hijacking attacks with almost-zero false positive rates. We conclude through experiments on different tasks that the simplicity of our approach we name \textit{SplitOut} makes it a more viable and reliable alternative compared to the earlier detection methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
