为联邦学习中的异常检测设计了新型注意力自编码聚合方法。
Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection
- 提出两种针对注意力自编码器的随机聚合函数。
- 在KDDCUP10数据集上F1提升2.9%，AUC ROC提升5.1%。
- 适合隐私保护下的分布式异常检测场景使用。
去中心化数据环境中的异常检测对许多机器学习应用构成挑战，尤其在数据无法共享的场景下。近年来，联邦异常检测有所进展，部分基于自编码器网络。引入注意力机制可提升自编码器效率，但其在联邦学习中的应用仍不成熟，主要因缺乏适用于此类网络的合理聚合函数。本文提出两种专为注意力自编码器设计的新聚合函数，更好保留网络中记忆模块所学信息。我们在KDDCUP10数据集上评估了该方法，结果显示，与传统自编码器相比，所提方法在F1分数上最高提升2.9%，在AUC ROC上最高提升5.1%。
原文摘要 · Abstract (English)
Outlier detection in decentralized data environments is a challenging task for many machine learning implementations, particularly in settings where data cannot be shared. Recently, there have been advances in federated outlier detection, some of which are based on the use of autoencoder networks. The introduction of attention mechanisms to autoencoders boosts their efficiency. However, the application of attention-based models in federated learning remains underdeveloped due to the absence of proper aggregation functions for these types of networks. In our work, we propose two novel aggregation functions tailored for attention-based autoencoders, which better preserve the learned information stored within the memory modules of these networks. We evaluated our approach on the KDDCUP10 dataset, and we showed that the proposed methods achieve up to 2.9\% and 5.1\% better results for F1 score and AUC ROC respectively when compared to traditional autoencoders.
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