Search Results for author: Madhusanka Liyanage

Found 8 papers, 1 papers with code

Need of 6G for the Metaverse Realization

no code implementations28 Dec 2022 Bartlomiej Siniarski, Chamitha De Alwis, Gokul Yenduri, Thien Huynh-The, GÜrkan GÜr, Thippa Reddy Gadekallu, Madhusanka Liyanage

The concept of the Metaverse aims to bring a fully-fledged extended reality environment to provide next generation applications and services.

Edge-computing

A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases, Challenges and Research Directions

no code implementations27 Apr 2022 Thulitha Senevirathna, Vinh Hoa La, Samuel Marchal, Bartlomiej Siniarski, Madhusanka Liyanage, Shen Wang

With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies.

Decision Making Edge-computing +2

Roadmap for Edge AI: A Dagstuhl Perspective

no code implementations27 Nov 2021 Aaron Yi Ding, Ella Peltonen, Tobias Meuser, Atakan Aral, Christian Becker, Schahram Dustdar, Thomas Hiessl, Dieter Kranzlmuller, Madhusanka Liyanage, Setareh Magshudi, Nitinder Mohan, Joerg Ott, Jan S. Rellermeyer, Stefan Schulte, Henning Schulzrinne, Gurkan Solmaz, Sasu Tarkoma, Blesson Varghese, Lars Wolf

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI.

Edge-computing

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

no code implementations8 Oct 2021 Thippa Reddy Gadekallu, Quoc-Viet Pham, Thien Huynh-The, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Madhusanka Liyanage

In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data.

Federated Learning

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