Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection
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Computer Science > Machine Learning
Title:Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection
Abstract: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.
| Comments: | Accepted and presented at the 3rd International Conference on Federated Learning Technologies and Applications (FLTA 2025) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.08906 [cs.LG] |
| (or arXiv:2608.08906v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.08906
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 3rd International Conference on Federated Learning Technologies and Applications (FLTA 2025) |
| Related DOI: | https://doi.org/10.1109/FLTA67013.2025.11336743
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