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Trustworthy AI and Mobile Generative AI for 6G Networks and Smart Industry Applications

Reference number
Coordinator Mittuniversitetet
Funding from Vinnova SEK 240 000
Project duration November 2024 - November 2025
Status Ongoing
Venture International individual mobility within cutting-edge technology
Call Closed offer - International individual mobility for cutting-edge technology 2024

Purpose and goal

Project aims to develop trustworthy AI solutions for automated industrial systems. In addition, the project will explore the integrating of Mobile Generative AI, it enhances real-time decision-making and dynamic content generation within 6G networks, optimizing adaptability, efficiency, and security through on-device processing and reduced latency. This MIUN and NTU´s collaboration drives trustworthy AI innovation, connecting research with real-world applications for safer, smarter industries.

Expected effects and result

Outcomes include AI-driven security measures, transparent decision protocols, and ethical governance, increasing safety, efficiency, and reliability for industry stakeholders. Seminars, workshops, and joint educational programs will equip researchers, students, and industry professionals with insights into secure AI deployment and governance, bridging academia with industry needs and fostering partnerships between Sweden and Singapore.

Planned approach and implementation

During the exchange, Dr. Kyi Thar will work with Prof. Dusit Niyato’s team on trustworthy AI for 6G networks and initiate new research proposals. Key activities include AI optimization, studying trustworthy AI and Mobile Generative AI, and aligning with the EU AI Act. Collaboration will continue remotely after the exchange, ensuring progress and meeting VINNOVA’s requirements. New MSc thesis proposals, joint research, and funding applications will have lasting impacts for NTU and MIUN.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 11 November 2024

Reference number 2024-03570