[2023 - 2025]

@article{Asad2024b,

title = {Federated Learning for Secure and Efficient Vehicular Communications in Open RAN},

author = {Muhammad Asad and Saima Shaukat and Jin Nakazato and Ehsan Javanmardi and Manabu Tsukada},

url = {https://rdcu.be/d7RSW},

doi = {10.1007/s10586-024-04932-3},

issn = {1386-7857},

year  = {2025},

date = {2025-01-28},

urldate = {2024-11-25},

journal = {Cluster Computing},

volume = {28},

number = {211},

abstract = {This paper presents a comprehensive exploration of federated learning applied to vehicular communications within the context of Open RAN. Through an in-depth review of existing literature and analysis of fundamental concepts, critical challenges are identified within the current methodologies employed in this sphere. A novel framework is proposed to address these shortcomings, fundamentally based on federated learning principles. This framework aims to enhance security and efficiency in vehicular communications, leveraging the flexibility of Open RAN architecture. The paper further delves into a rigorous justification of the proposed solution, highlighting its potential impact and the improvements it could bring to vehicular communications. Ultimately, this study provides a roadmap for future research in applying federated learning for more secure and efficient vehicular communications in Open RAN, opening up new avenues for exploration in this exciting interdisciplinary domain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Asad2024,

title = {Secure and Efficient Blockchain-based Federated Learning Approach For VANETs},

author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Naren Bao and Manabu Tsukada},

doi = {10.1109/JIOT.2023.3322221},

issn = {2327-4662},

year  = {2024},

date = {2024-03-01},

urldate = {2023-10-05},

journal = {IEEE Internet of Things Journal},

volume = {11},

issue = {5},

pages = {9047-9055},

abstract = {The rapid increase in the number of connected vehicles on roads has made Vehicular Ad-hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated Learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This paper proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a Secure and Efficient Blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a Homomorphic Encryption System (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experiment results show the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Asad2023b,

title = {Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey},

author = {Muhammad Asad and Saima Shaukat and Dou Hu and Zekun Wang and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},

doi = {10.3390/s23177358},

issn = {1424-8220},

year  = {2023},

date = {2023-08-23},

urldate = {2023-08-23},

journal = {Sensors},

volume = {23},

number = {17},

abstract = {This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}