Efficient and Trustworthy Federated Learning-Based Explainable Anomaly Detection: Challenges, Methods, and Future Directions

Do Thu Ha, Ta Phuong Bac, Kim Duc Tran () and Kim Phuc Tran
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Do Thu Ha: University of Lille, ENSAIT, ULR 2461 - GEMTEX - Génie et Matériaux Textiles
Ta Phuong Bac: International Research Institute for Artificial Intelligence and Data Science, Dong A University
Kim Duc Tran: International Research Institute for Artificial Intelligence and Data Science, Dong A University
Kim Phuc Tran: University of Lille, ENSAIT, ULR 2461 - GEMTEX - Génie et Matériaux Textiles

A chapter in Artificial Intelligence for Smart Manufacturing, 2023, pp 145-166 from Springer

Abstract: Abstract Artificial Intelligence (AI) and especially Machine Learning (ML) are the driving energy behind industrial and technological transformation. With the transition from industry 4.0 to 5.0, smart manufacturing proves the efficiency in industry, where systems become increasingly complex, producing massive data, necessitating more demand for transparency, privacy, and performance. Federated learning has demonstrated its effectiveness in various applications, however, there are still exist certain challenges that should be addressed. Thus, in this chapter, a comprehensive perspective on federated learning-based anomaly detection is provided. The problems have posed concerns and should be taken into account when researching and deploying. Then, our perspectives about efficient and trustworthy federated learning-based explainable anomaly detection systems are demonstrated as an end-to-end unified framework. Finally, to provide a complete picture of future research direction, the quantum aspect is introduced in the subject of machine learning.

Keywords: Federated learning; Explainable anomaly detection; Blockchain machine learning; Graph transformer network; Quantum computing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-30510-8_8

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