Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detection

arXiv:2608.04753 · cs.LG · Submitted 2026-08-05 · Read on arXiv

Mihailo Ilić, Miloš Savić, Vladimir Kurbalija, Mirjana Ivanović, Giancarlo Fortino, Dušan Jakovetić

cs.LG

Submitted: 2026-08-05

Comments: Submitted to the 4th IEEE International Conference on Federated Learning Technologies and Applications (FLTA 2026)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Federated Learning — 25× in this paper · explained in 19 episodes
• Clustering — 16× in this paper · explained in 2 episodes
• AUC — 14× in this paper · explained in 2 episodes
• Representation Learning — 13× in this paper · explained in 3 episodes
• Decoder — 10× in this paper · explained in 2 episodes
• Attention Mechanism — 6× in this paper · explained in 3 episodes
• F1 Score — 6× in this paper · explained in 4 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Editor — 4× in this paper · explained in 2 episodes
• K-means Clustering — 4× in this paper · explained in 3 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Federated Learning (FL) — 3× in this paper · explained in 12 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Conference on Federated Learning Technologies and Applications — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Hyperparameters — 2× in this paper · explained in 2 episodes
• Machine Learning (ML) — 2× in this paper · explained in 2 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 6 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Internet of Things — 1× in this paper
• Learning Paradigms — 1× in this paper · explained in 2 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 5 episodes

Sources

• A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms

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