CONFERENCE AIMS AND OBJECTIVES

Abstract

Software-Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm that enables multiple parties to collaboratively train a model without sharing their data. With the upcoming Sixth Generation (6G) era, FL is expected to adapt a more prevalent role as a potential solution to overcome the challenges of data privacy, AI, security and scalability in distributed and heterogeneous systems. Presently, research works in the security domain of FL enabled 6G communication are widely pursued. However, the outcome of research efforts is critically dependent upon the concepts and platforms used during analysis and evaluation. Therefore, this study initiates by focusing on the requirements of analysis in the security of distributed and heterogeneously involved multiple entities in FL enabled 6G. Thereby, this study comprehensively reviews and identifies the potential Conceptual Techniques and Software Platforms for analysis and evaluation in security-related areas of FL enabled 6G communication. Further, this study highlights major challenges faced during the analysis in the security of FL enabled 6G. Finally, this review deliberates upon the potential open research issues AI, security, and that can be pursued using the identified techniques and platforms.

Profile

Dr. Kashif Nisar has done Post-Doctoral from Auckland University of Technology, Auckland, New Zealand. Before, he completed his PhD as a candidate with fully funding at the Universiti Teknologi PETRONAS, Malaysia. Through his major in Computer Network and Information Technology; he has obtained solid training in Research and Development (R&D), writing funding proposal, journal publication, and as a consultant. Currently, Dr. Nisar is serving as a Lecturer at the University of Notre Dame, Sydney Campus, New South Wales, Australia. In 2014, he has served as a Guest Professor at Fernuniversität Hagen, Germany, fully funded by DAAD. He holds a number of visiting professor positions in well-known universities such the McMaster University, Hamilton, ON, Canada, University of Auckland, New Zealand, Hanyang University, South Korea, and Waseda University, Tokyo, Japan.  Dr. Nisar has been published 250+ research papers in many high impact journals and well reputed international conferences proceeding in the area of Computer Network. His research interests include Future Internet (FI), Artificial Intelligence (AI), Information Centric Network (ICN), Content-Centric Networking (CCN), Fourth Industrial Revolution (IR 4.0), Quantum Network, Information Security & Privacy, Network/Cyber Security, Digital Forensics, Applied Cryptography, Vehicular Clouds, Cloud & Edge Computing, and Blockchain. Currently, he is working on Future Networks, IoT security and API security and he is also working closely with Industry. Dr. Nisar is the member of many professional organizations from academia and industry including Senior Member of IEEE (Founding IEEE Vice-Chair, Subsection), member of ACM, ACM-SIGMOBILE, ISOC, Engineers Australia, IAENG, Park Lab etc. and a fellow of APAN and ITU. Dr. Nisar is serving as an editorial board member for various journals including Computer Communications Elsevier, IEEE Internet Initiative, Wiley, and serves as reviewer for most of the IEEE transactions, IEEE Access, IEEE IoT, Springer and Elsevier Journals. He also serves as technical program committee member of various conferences such as IEEE GLOBECOM, IEEE R10 TENCOM, IEEE TrustCom, IEEE ICC, IEEE VTC, IEEE VNC, IEEE ICCVE, ICCCN, and so on. Also, he is serving as a guest editor for more than a dozen special issues in journals and magazines such as IEEE, Elsevier, Springer and Wiley.

Abstract

Artificial Intelligence and Security in Federated Learning Enabled 6G Era

Software-Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm that enables multiple parties to collaboratively train a model without sharing their data. With the upcoming Sixth Generation (6G) era, FL is expected to adapt a more prevalent role as a potential solution to overcome the challenges of data privacy, AI, security and scalability in distributed and heterogeneous systems. Presently, research works in the security domain of FL enabled 6G communication are widely pursued. However, the outcome of research efforts is critically dependent upon the concepts and platforms used during analysis and evaluation. Therefore, this study initiates by focusing on the requirements of analysis in the security of distributed and heterogeneously involved multiple entities in FL enabled 6G. Thereby, this study comprehensively reviews and identifies the potential Conceptual Techniques and Software Platforms for analysis and evaluation in security-related areas of FL enabled 6G communication. Further, this study highlights major challenges faced during the analysis in the security of FL enabled 6G. Finally, this review deliberates upon the potential open research issues AI, security, and that can be pursued using the identified techniques and platforms.