Federated Online Learning for Heavy Hitter Detection

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Silva, PR (Author) Other; Vinagre, J (Author) Other; Gama, João (Author) FEP

Conference proceedings International

Title: 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)

Pages: 4689-4695

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: 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>

Language: English

Type (Professor's evaluation): Scientific