Federated Online Learning for Heavy Hitter Detection

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Silva, PR (Autor) Outra; Vinagre, J (Autor) Outra; Gama, João (Autor) FEP

Ata de Conferência Internacional

Título: ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024)

Páginas: 4689-4695

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-017-6BW

DOI: 10.3233/faia241065

Abstract (EN): <jats:p>Effective anomaly detection in telecommunication networks is essential for securing digital transactions and supporting the sustainability of our global information ecosystem. However, the volume of data in such high-speed distributed environments imposes strict latency and scalability requirements on anomaly detection systems. This study focuses on distributed heavy hitter detection in telephone networks ¿ a critical component of network traffic analysis and fraud detection. We propose a federated version of the Lossy Counting algorithm and compare it to its centralized version. Our experimental results reveal that the federated approach can detect considerably more unique heavy hitters than the centralized method while enhancing privacy. Furthermore, Federated Lossy Counting does not need a large amount of centralized processing power since it can leverage the networked infrastructure with minimal impact on bandwidth and computing power.</jats:p>

Idioma: Inglês

Tipo (Avaliação Docente): Científica