在联邦学习中安全检测全局异常点，保护隐私且效果接近中心化方法。
Global Outlier Detection in a Federated Learning Setting with Isolation Forest
- 双服务器架构+数据掩码，防止敏感信息泄露。
- 通过置换机制隐藏数据归属，实现隐私保护下的异常检测。
- 适用于跨组织联邦学习，适合关注数据安全的团队。
我们提出一种新型策略，用于在联邦学习设置中检测全局异常点，特别针对跨多方（cross-silo）场景。该方法采用双服务器架构，客户端将掩码后的本地数据传输至其中一个服务器。数据掩码机制可防止敏感信息泄露，同时仍支持异常点识别。为进一步保障隐私，引入置换机制，使服务器无法知晓任一掩码数据点所属的客户端。服务器基于掩码数据执行异常检测，使用孤立森林（Isolation Forest）或其扩展版本，并将检测结果反馈给客户端，使其可在后续联邦训练前识别并移除本地异常数据。实验表明，该方法性能与在原始数据上进行集中式孤立森林处理相当。
原文摘要 · Abstract (English)
We present a novel strategy for detecting global outliers in a federated learning setting, targeting in particular cross-silo scenarios. Our approach involves the use of two servers and the transmission of masked local data from clients to one of the servers. The masking of the data prevents the disclosure of sensitive information while still permitting the identification of outliers. Moreover, to further safeguard privacy, a permutation mechanism is implemented so that the server does not know which client owns any masked data point. The server performs outlier detection on the masked data, using either Isolation Forest or its extended version, and then communicates outlier information back to the clients, allowing them to identify and remove outliers in their local datasets before starting any subsequent federated model training. This approach provides comparable results to a centralized execution of Isolation Forest algorithms on plain data.
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