ITU AI/ML in 5G Challenge – “Demonstration of machine learning function orchestrator (MLFO) via reference implementations (ITU-ML5G-PS-024)
[ITU-T Y.3172] specified machine learning function orchestrator (MLFO) as an architecture component for integration of AI/ML in future networks including 5G. Based on the requirements of multiple use cases and reference points as explained in the [ITU-T Y.Sup55], MLFO presents an interesting challenge of its reference implementation. The first part of the talk is intended to discuss the problems addressed by the MLFO, MLFO architecture, and important reference points. The second part of the talk will discuss the challenge to support the reference implementation of MLFO including specific concepts and evaluation criteria.
Speakers, Panelists and Moderators
SHAGUFTA HENNALecturerShagufta Henna is a lecturer with the Department of Computing Letterkenny Institute of Technology (LYIT), Ireland. She was a research fellow with the CONNECT - the Science Foundation Ireland Research Centre for Future Networks and Communications, Ireland from 2018 to 2019. Her work involved the commercialization of deep learning-based network management in an industry relevant environment in collaboration with an Industry partner (TRL 5). She received her doctoral degree in Computer Science from the University of Leicester, UK in 2013. Her PhD covered various algorithmic aspects of wireless networks. She has been involved in several EU and national level research projects including Horizon 2020, Science Foundation of Ireland, and Enterprise of Ireland. She is a senior member of the IEEE and is currently serving the editorial boards of IEEE Access, EURASIP Journal on Wireless Communications and Networking, IEEE Future Directions, and Springer Human-centric Computing and Information Sciences. Her current research interests include deep learning, edge intelligence, network security, machine learning for future generation networks, and big data analytics.