The International workshop on Machine Learning and Blockchain for Smart Society(MLBSS-2023)
in conjunction with The 24th International Conference on Distributed Computing & Networking (ICDCN 2023)
in conjunction with The 24th International Conference on Distributed Computing & Networking (ICDCN 2023)
ICDCN is a premier international conference dedicated to addressing advances in Distributed Computing and Communication Networks. Over the years, it has become a leading forum for disseminating the latest research results in these fields. The 24th edition of this conference will be held in a historical city, Kharagpur. MLBSS 2023 workshop is co-located with ICDCN 2023.
The scope of this workshop will cover relevant research focusing on the incorporation and use of advanced artificial intelligence, machine learning techniques and blockchain to support and enhance smart transportation, smart grid, safety applications, smart homes, smart city and smart healthcare solutions. The workshop invites academia, research professionals, industry and government entities to submit:
Original research papers on blockchain and AI/ML based smart healthcare system, intelligent transportation, smart grid, safety applications, smart homes.
Demos (hands-on or videos) of testbeds/experiences/proof of concepts/prototype of blockchain and AI/ML based systems.
Mathematical foundations for AI/ML and blockchain based solutions
High assurance security architectures for smart society applications
Blockchain based security and privacy solutions for smart society
AI/ML based risk assessment approaches
Identity and access management using AI/ML approaches
Trust management for smart society
Human factors and blockchain based security for development of smart society
Intrusion and anomaly detection for smart society applications
Decision making and reinforcement learning for smart society applications
Efficient, scalable processing of huge amount of data in smart society
Security and privacy for edge intelligence in 5G and beyond networks
Secure RNN Inference
Privacy in Federated Learning
5/6G, UAV
Homomorphically Encrypted DNN
Trustworthy ML
Secure Poisson Regression
Multi-Party Cryptographic Collaborative Learning
Privacy-preserving machine learning
Mathematical foundations for AI/ML and blockchain based solutions