Hyper-Specific Sub-Field Selection: Container-Based Anomaly Detection via Federated Meta-Learning and Graph Neural Networks

**Selected Sub-Field:** Utilizing Federated Meta-Learning and Graph Neural Networks for Detecting Subtle Anomalies in Containerized Microservice Architectures.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-11
DOI
https://doi.org/10.5281/zenodo.16791857
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.00
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article

Hyper-Specific Sub-Field Selection: Container-Based Anomaly Detection via Federated Meta-Learning and Graph Neural Networks

Zenodo (CERN European Organization for Nuclear Research)
Software System Performance and Reliability
article

Hyper-Specific Sub-Field Selection: Container-Based Anomaly Detection via Federated Meta-Learning and Graph Neural Networks

article en

Abstract

**Selected Sub-Field:** Utilizing Federated Meta-Learning and Graph Neural Networks for Detecting Subtle Anomalies in Containerized Microservice Architectures.

Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 49%
Software System Performance and Reliability
DOIOpen Access PDFOpenAlex
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Hyper-Specific Sub-Field Selection: Container-Based Anomaly Detection via Federated Meta-Learning and Graph Neural Networks · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS