Publication
2025
Muhammad Asad, Saima Shaukat, Jin Nakazato, Ehsan Javanmardi, Manabu Tsukada, "Federated Learning for Secure and Efficient Vehicular Communications in Open RAN", In: Cluster Computing, vol. 28, no. 211, 2025, ISSN: 1386-7857.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
2024
Dou Hu, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Maruta Kazuki, Manabu Tsukada, "A Research of Kalman Filter enabled Beam Tracking for Multiple Vehicles", ASPIRE Workshop 2024 in conjunction with the IEICE General Conference, Hiroshima, Japan, 2024, ISBN: 2188-5079.Workshop | Abstract | BibTeX | Links:
@workshop{Hu2024,
title = {A Research of Kalman Filter enabled Beam Tracking for Multiple Vehicles},
author = {Dou Hu and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Maruta Kazuki and Manabu Tsukada},
url = {https://www.ieice.org/publications/proceedings/bin/pdf_link.php?fname=15.pdf&iconf=ASPIRE_WS&year=2024&vol=80&number=P-15&lang=E?.pdf},
doi = {10.34385/proc.80.P-15},
isbn = {2188-5079},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
booktitle = {ASPIRE Workshop 2024 in conjunction with the IEICE General Conference},
address = {Hiroshima, Japan},
abstract = {In the era of Beyond 5G, the significance of interdisciplinary research has become increasingly important. Within this context, the Kalman filter, a technology integral to self-positioning estimation in autonomous driving, is already being adopted in various societal applications. This study proposes a method wherein beam tracking, in conjunction with the Kalman filter, is an alternative to GPS in specific scenarios. This research is particularly relevant in environments such as intersections flanked by high-rise buildings, where GPS signals are prone to interference.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
In the era of Beyond 5G, the significance of interdisciplinary research has become increasingly important. Within this context, the Kalman filter, a technology integral to self-positioning estimation in autonomous driving, is already being adopted in various societal applications. This study proposes a method wherein beam tracking, in conjunction with the Kalman filter, is an alternative to GPS in specific scenarios. This research is particularly relevant in environments such as intersections flanked by high-rise buildings, where GPS signals are prone to interference.
Muhammad Asad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, Naren Bao, Manabu Tsukada, "Secure and Efficient Blockchain-based Federated Learning Approach For VANETs", In: IEEE Internet of Things Journal, vol. 11, iss. 5, pp. 9047-9055, 2024, ISSN: 2327-4662.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
Ryo Iwaki, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Kazuki Maruta, Manabu Tsukada, Hideya Ochiai, Hiroshi Esaki, "Optimizing mmWave Beamforming for High-Speed Connected Autonomous Vehicles: An Adaptive Approach", 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Poster, 2024.Miscellaneous | Abstract | BibTeX | Links:
@misc{Iwaki2024,
title = {Optimizing mmWave Beamforming for High-Speed Connected Autonomous Vehicles: An Adaptive Approach},
author = {Ryo Iwaki and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_CCNC2023_poster_Iwaki.pdf},
year = {2024},
date = {2024-01-06},
urldate = {2024-01-06},
abstract = {The commercialization of 5G has been initiated for a while. Furthermore, millimeter wave (mmWave) has been introduced to small cells with small coverage due to its strong linearity and non-winding characteristics. On the other hand, in connected autonomous vehicles (CAVs), where various traffic systems can cooperatively perform recognition, decision-making, and execution, communication is assumed to be always connected. Therefore, to use low latency mmWave for high-speed moving CAV, beamforming in 5G cannot follow them at high speed. This paper proposes an improved beam tracking algorithm for high-speed CAVs, which can be evaluated in a more general environment using a traffic simulator. We proposed an adaptive algorithm for a general road environment by increasing the number of beam searches and search dimensions.},
howpublished = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The commercialization of 5G has been initiated for a while. Furthermore, millimeter wave (mmWave) has been introduced to small cells with small coverage due to its strong linearity and non-winding characteristics. On the other hand, in connected autonomous vehicles (CAVs), where various traffic systems can cooperatively perform recognition, decision-making, and execution, communication is assumed to be always connected. Therefore, to use low latency mmWave for high-speed moving CAV, beamforming in 5G cannot follow them at high speed. This paper proposes an improved beam tracking algorithm for high-speed CAVs, which can be evaluated in a more general environment using a traffic simulator. We proposed an adaptive algorithm for a general road environment by increasing the number of beam searches and search dimensions.
2023
岩城燎, 中里仁, 小澤爽仁, 丸田一輝, ムハマド アサード, エッサン ジャワーンマーディ, 塚田学, 落合秀也, 江崎浩, "一般道路環境における高速ビーム追従の適応的アルゴリズムの提案", 無線通信システム研究会（RCS）, 熊本県, 2023.Conference | Abstract | BibTeX | Links:
@conference{Iwaki2023,
title = {一般道路環境における高速ビーム追従の適応的アルゴリズムの提案},
author = {岩城燎 and 中里仁 and 小澤爽仁 and 丸田一輝 and ムハマド アサード and エッサン ジャワーンマーディ and 塚田学 and 落合秀也 and 江崎浩},
url = {https://tlab.hongo.wide.ad.jp/papers/2023_RCS_Iwaki.pdf},
year = {2023},
date = {2023-11-15},
urldate = {2023-11-15},
booktitle = {無線通信システム研究会（RCS）},
address = {熊本県},
abstract = {2019年より世界中でサービスが開始された5Gでは，ミリ波が移動体通信として初めて導入された．ミリ波は直進性が強く，回り込みしない特徴からカバレッジが小さいスモールセルへの導入がされている．一方で，様々な交通システムが協調的に認知，判断，実行を担える協調型自動運転では，通信に常時繋がることが前提とされている．高速移動する車両に対して低遅延であるミリ波を用いるためには5Gにおけるビームフォーミングでは高速で追従できない課題がある．そこで本稿では，その課題の1つの解決策となる，高速で移動する車両に対し高速にビーム追従を行うビーム追従アルゴリズムについて，交通シミュレータと連携させることでより一般的な環境で評価することを行うことを可能とする．さらに，ビーム探索数の増加と探索次元の増加方式を提案し，一般的な道路環境にて評価し，適応的なアルゴリズムであることを示した．
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2019年より世界中でサービスが開始された5Gでは，ミリ波が移動体通信として初めて導入された．ミリ波は直進性が強く，回り込みしない特徴からカバレッジが小さいスモールセルへの導入がされている．一方で，様々な交通システムが協調的に認知，判断，実行を担える協調型自動運転では，通信に常時繋がることが前提とされている．高速移動する車両に対して低遅延であるミリ波を用いるためには5Gにおけるビームフォーミングでは高速で追従できない課題がある．そこで本稿では，その課題の1つの解決策となる，高速で移動する車両に対し高速にビーム追従を行うビーム追従アルゴリズムについて，交通シミュレータと連携させることでより一般的な環境で評価することを行うことを可能とする．さらに，ビーム探索数の増加と探索次元の増加方式を提案し，一般的な道路環境にて評価し，適応的なアルゴリズムであることを示した．
Naren Bao, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Manabu Tsukada, "Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification", The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things, Nagoya, Japan, 2023.Workshop | Abstract | BibTeX | Links:
@workshop{Bao2023b,
title = {Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification},
author = {Naren Bao and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1145/3627050.3631566},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {The concept of a trusted inter-reality, where physical and virtual worlds seamlessly converge, represents a paradigm shift in how digital identities are formed and managed. This paper explores the complex landscape of system architectures designed to enable secure and user-centric digital identification within interconnected realities. Our survey focuses on user-centric security, recognizing the prevalence of wearable devices and immersive technologies in inter-reality environments. We advocate for user-friendly authentication methods and privacy-preserving techniques that prioritize user control within the trust model. Furthermore, we delve into the influence of social and cultural factors, particularly age and gender, on the shaping of digital identity within interconnected realities. We argue in favor of adaptable system architectures that respect generational and gender diversity. In conclusion, we emphasize the alignment of system architectures with these principles to promote a secure, user-centric, and culturally sensitive digital identity experience. This research contributes to the ongoing discourse on digital identification in interconnected realities, providing actionable guidance for stakeholders in the evolving landscape of trusted inter-reality.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
The concept of a trusted inter-reality, where physical and virtual worlds seamlessly converge, represents a paradigm shift in how digital identities are formed and managed. This paper explores the complex landscape of system architectures designed to enable secure and user-centric digital identification within interconnected realities. Our survey focuses on user-centric security, recognizing the prevalence of wearable devices and immersive technologies in inter-reality environments. We advocate for user-friendly authentication methods and privacy-preserving techniques that prioritize user control within the trust model. Furthermore, we delve into the influence of social and cultural factors, particularly age and gender, on the shaping of digital identity within interconnected realities. We argue in favor of adaptable system architectures that respect generational and gender diversity. In conclusion, we emphasize the alignment of system architectures with these principles to promote a secure, user-centric, and culturally sensitive digital identity experience. This research contributes to the ongoing discourse on digital identification in interconnected realities, providing actionable guidance for stakeholders in the evolving landscape of trusted inter-reality.
Yusuke Sugizaki, Hideya Ochiai, Muhammad Asad, Manabu Tsukada, Hiroshi Esaki, "Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models", In: The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023), Aveiro, Portugal, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Sugizaki2023,
title = {Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models},
author = {Yusuke Sugizaki and Hideya Ochiai and Muhammad Asad and Manabu Tsukada and Hiroshi Esaki},
doi = {10.1109/WF-IoT58464.2023.10539517},
year = {2023},
date = {2023-10-12},
urldate = {2023-10-12},
booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},
address = {Aveiro, Portugal},
abstract = {Although Wi-Fi signals have been used for localization, many existing methods require gathering Wi-Fi information about the area in advance. This study proposed a novel system in which wireless ad-hoc federated learning is used to learn localization models and create maps cooperatively during regular movement. In this system, a combination of classification models is used to perform localization from Wi-Fi signal strength measured as received signal strength indicator (RSSI). In this study, RSSI data in a real-world Wi-Fi environment were collected to train and test localization models. The proposed method achieved localization accuracy between 91.30% and 96.11 %, which demonstrated the ability of the method to train localization models collaboratively.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Although Wi-Fi signals have been used for localization, many existing methods require gathering Wi-Fi information about the area in advance. This study proposed a novel system in which wireless ad-hoc federated learning is used to learn localization models and create maps cooperatively during regular movement. In this system, a combination of classification models is used to perform localization from Wi-Fi signal strength measured as received signal strength indicator (RSSI). In this study, RSSI data in a real-world Wi-Fi environment were collected to train and test localization models. The proposed method achieved localization accuracy between 91.30% and 96.11 %, which demonstrated the ability of the method to train localization models collaboratively.
Dou Hu, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Manabu Tsukada, Kazuki Maruta
, "Extended Kalman filter based beam tracking for vehicle position and velocity estimation under intersection scenario", 革新的無線通信技術に関する横断型研究会（MIKA）, 沖縄県, 2023.Conference | Abstract | BibTeX
@conference{Dou2023,
title = {Extended Kalman filter based beam tracking for vehicle position and velocity estimation under intersection scenario},
author = {Dou Hu and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Manabu Tsukada and Kazuki Maruta
},
year = {2023},
date = {2023-10-10},
urldate = {2023-10-10},
booktitle = {革新的無線通信技術に関する横断型研究会（MIKA）},
address = {沖縄県},
abstract = {As typified by the IoT, mobile traffic continues to increase with the spread of devices equipped with wireless communication functions. Deploying small cell base stations (BSs) is known to straight forward way to efficiently support such traffic. Meanwhile, the facility cost increases when large numbers of BSs are deployed in a fixed manner. It is possible to construct an efficient wireless network by installing BS functions in moving objects such as vehicles and UAVs, and allowing them to move autonomously or activate wireless functions to follow the traffic demand. This paper proposes a method for estimating propagation channels and vehicle position/velocity information at intersections based on vehicle-oriented beam control using a Kalman filter.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
As typified by the IoT, mobile traffic continues to increase with the spread of devices equipped with wireless communication functions. Deploying small cell base stations (BSs) is known to straight forward way to efficiently support such traffic. Meanwhile, the facility cost increases when large numbers of BSs are deployed in a fixed manner. It is possible to construct an efficient wireless network by installing BS functions in moving objects such as vehicles and UAVs, and allowing them to move autonomously or activate wireless functions to follow the traffic demand. This paper proposes a method for estimating propagation channels and vehicle position/velocity information at intersections based on vehicle-oriented beam control using a Kalman filter.
Muhammad Asad, Saima Shaukat, Dou Hu, Zekun Wang, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey", In: Sensors, vol. 23, no. 17, 2023, ISSN: 1424-8220.Journal Article | Abstract | BibTeX | Links:
@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}
}
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.
Zekun Wang, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Manabu Tsukada, "Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning", In: 14th International Conference on Ubiquitous and Future Networks (ICUFN2023), pp. 1-6, Paris, France, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Wang2023,
title = {Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning},
author = {Zekun Wang and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371685830_Overcoming_Environmental_Challenges_in_CAVs_through_MEC-based_Federated_Learning/links/64a420ea8de7ed28ba7465c7/Overcoming-Environmental-Challenges-in-CAVs-through-MEC-based-Federated-Learning.pdf},
doi = {10.1109/ICUFN57995.2023.10200688},
year = {2023},
date = {2023-07-04},
urldate = {2023-07-04},
booktitle = {14th International Conference on Ubiquitous and Future Networks (ICUFN2023)},
pages = {1-6},
address = {Paris, France},
abstract = {Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models' caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models' caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.
Dou Hu, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Manabu Tsukada
, "An Extended Kalman Filter Enabled Beam Tracking Framework in Intersection Management", European Conference on Networks and Communications (EuCNC) & 6G Summit Poster, 2023.Miscellaneous | Abstract | BibTeX | Links:
@misc{Hu2023,
title = {An Extended Kalman Filter Enabled Beam Tracking Framework in Intersection Management},
author = {Dou Hu and Jin Nakazato and Ehsan Javanmardi and Muhammad Asad and Manabu Tsukada
},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371358188_An_Extended_Kalman_Filter_Enabled_Beam_Tracking_Framework_in_Intersection_Management/links/64807e24b3dfd73b776baeed/An-Extended-Kalman-Filter-Enabled-Beam-Tracking-Framework-in-Intersection-Management.pdf},
year = {2023},
date = {2023-06-06},
urldate = {2023-06-06},
address = {Gothenburg, Sweden},
abstract = {Recently, vehicle-to-everything (V2X) has been at- tracting attention for its potential to improve traffic safety and increase traffic volume worldwide, improving the accuracy of data and parameters collected from moving vehicles is widely discussed in the V2X. The most common technique of GPS may not be efficient during some specific scenarios, like some intersections full of skyscrapers, or some special terrains with obstacles. In such cases, GPS technology has a longer detection period and lower tracking accuracy, so beam tracking can be a fast and efficient solution in these circumstances. Therefore we propose an anti-diverge extend Kalman filter-enabled beam tracking method in V2X to help the intersection management. The numerical results show that our method has the ability to resist the Kalman filter’s divergence and can detect data in an accurate manner.},
howpublished = {European Conference on Networks and Communications (EuCNC) & 6G Summit Poster},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Recently, vehicle-to-everything (V2X) has been at- tracting attention for its potential to improve traffic safety and increase traffic volume worldwide, improving the accuracy of data and parameters collected from moving vehicles is widely discussed in the V2X. The most common technique of GPS may not be efficient during some specific scenarios, like some intersections full of skyscrapers, or some special terrains with obstacles. In such cases, GPS technology has a longer detection period and lower tracking accuracy, so beam tracking can be a fast and efficient solution in these circumstances. Therefore we propose an anti-diverge extend Kalman filter-enabled beam tracking method in V2X to help the intersection management. The numerical results show that our method has the ability to resist the Kalman filter’s divergence and can detect data in an accurate manner.
Muhammad Asad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems", In: Applied Sciences , 2023, ISSN: 2076-3417.Journal Article | Abstract | BibTeX | Links:
@article{Asad2023,
title = {A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems},
author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
doi = {10.3390/app13106201},
issn = {2076-3417},
year = {2023},
date = {2023-05-18},
urldate = {2023-05-18},
journal = {Applied Sciences },
abstract = {Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.
2022
Muhammad Asad, Muhammad Aslam, Syeda Fizzah Jilani, Saima Shoukat, Manabu Tsukada, "SHFL: K-Anonymity-based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems", In: Future Internet, vol. 14, no. 11, 2022, ISBN: 1999-5903.Journal Article | Abstract | BibTeX | Links:
@article{Asad2022,
title = {SHFL: K-Anonymity-based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems},
author = {Muhammad Asad and Muhammad Aslam and Syeda Fizzah Jilani and Saima Shoukat and Manabu Tsukada},
url = {https://www.mdpi.com/1999-5903/14/11/338},
doi = {10.3390/fi14110338},
isbn = {1999-5903},
year = {2022},
date = {2022-11-18},
urldate = {2022-11-18},
journal = {Future Internet},
volume = {14},
number = {11},
abstract = {Dynamic and smart infrastructures of the Internet of Things (IoT) allow the development of smart healthcare systems. These smart healthcare systems are equipped with mobile health and embedded healthcare sensors to provide a broad range of healthcare applications. These IoT applications provide the key availability of clients’ health information. However, the boost in the number of mobile devices and social networks intends to share the locations without the clients’ concern. In this regard, Federated Learning (FL) is an emerging paradigm of decentralized machine learning that guarantees to train a shared global model without compromising client data privacy. To this end, in this paper, we propose a K-Anonymity-based Secure Hierarchical Federated Learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates with multiple directly and indirectly connected devices hierarchically. In particular, the proposed SHFL formulates the location-based services (LBS)-hierarchical clusters to execute distributed FL. Besides, the proposed SHFL utilizes the K-Anonymity method to hide the location of the clusters’ devices. In the end, we evaluate the performance of the proposed SHFL by configuring the different hierarchical networks with multiple model architectures and datasets. The experiments validate that the proposed SHFL provides a suitable generalization to enable network scalability of accurate healthcare systems without compromising data and location privacy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dynamic and smart infrastructures of the Internet of Things (IoT) allow the development of smart healthcare systems. These smart healthcare systems are equipped with mobile health and embedded healthcare sensors to provide a broad range of healthcare applications. These IoT applications provide the key availability of clients’ health information. However, the boost in the number of mobile devices and social networks intends to share the locations without the clients’ concern. In this regard, Federated Learning (FL) is an emerging paradigm of decentralized machine learning that guarantees to train a shared global model without compromising client data privacy. To this end, in this paper, we propose a K-Anonymity-based Secure Hierarchical Federated Learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates with multiple directly and indirectly connected devices hierarchically. In particular, the proposed SHFL formulates the location-based services (LBS)-hierarchical clusters to execute distributed FL. Besides, the proposed SHFL utilizes the K-Anonymity method to hide the location of the clusters’ devices. In the end, we evaluate the performance of the proposed SHFL by configuring the different hierarchical networks with multiple model architectures and datasets. The experiments validate that the proposed SHFL provides a suitable generalization to enable network scalability of accurate healthcare systems without compromising data and location privacy.
