Blockchain-Enabled Federated Learning Approach for Vehicular Networks

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. The integration of these technologies enables the creation of a decentralized vehicular network. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Initial experiments show that compared to conventional decentralized federated learning techniques, our proposed approach significantly enhances the performance and security of vehicular networks. The system's accuracy stands at 91.92\%. While this may appear to be low in comparison to state-of-the-art federated learning models, our work is noteworthy because, unlike others, it was achieved in a malicious vehicle setting. Despite the challenging environment, our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks.Comment: 7 page

Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events

Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure

6G AĞLARDA ARAÇLARIN HABERLEŞMESİ: GELİŞİMİ, ETKİNLEŞTİRİCİ TEKNOLOJİLER VE GÜVENLİK

Yüksek veri hızı, kısa gecikme süresi ve düşük enerji tüketimini içeren amaçlarıyla 5G haberleşme teknolojilerinin standartlaşma çalışmaları sürmekteyken; yüksek bağlantılılığı ve kesintisiz haberleşmeyi esas alan, yeniden konfigüre edilebilir akıllı yüzeylerin yer alabildiği ve çeşitli yörünge seviyelerinde uydu veya insansız hava araçlarının daha etkin şekilde entegre olduğu, aynı zamanda güvenlik unsurunun haberleşme gereksinimleri kadar öne çıktığı ve daha zorlayıcı anahtar performans göstergelerine sahip 6G teknolojileri konusunda kuramsal ve deneysel araştırmalar büyük bir hızla sürdürülmektedir. 6G haberleşme ağlarındaki kritik kullanım senaryolarından biri olarak değerlendirilen araçların haberleşmesi için ortaya çıkabilecek güvenlik problemleri ve olası çözümleri ise önemli araştırma alanlarından birini oluşturmaktadır. Haberleşmenin yüksek veri hızında, kesintisiz ve güvenli şekilde gerçekleşmesi hedeflendiğinde; özellikle otonom araçlar için yol güvenliği ve konvoy sürüşü gibi araçlar arası haberleşmenin doğasından kaynaklanan zorluk senaryolar ortaya çıkmaktadır. Otonom sürüş konusunda haberleşme güvenliği ön planda yer alırken, araçtan her şeye (vehicle-to-everyhing, V2X) haberleşme ve araçların interneti (internet of vehicles, IoV) sistemlerinin tasarımlarında blok zincir, kuantum hesaplama ve yapay zekâ teknikleri dâhil yenilikçi yöntem ve algoritmaların geliştirilmesinin önemi artmaktadır. Bu makale ile 6G haberleşme sistemlerinin önemli bir parçasını oluşturacak araç haberleşmesinin gelişimi, yenilikçi teknolojileri ve güvenlik konularına odaklanılmakta, literatürde yapılan çalışmalar derlenerek yeni gelişme alanları ve odak noktaları detaylandırılmaktadır

Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning

Terabytes of data are collected every day by wind turbine manufacturers from their fleets. The data contain valuable real-time information for turbine health diagnostics and performance monitoring, for predicting rare failures and the remaining service life of critical parts. And yet, this wealth of data from wind turbine fleets remains inaccessible to operators, utility companies, and researchers as manufacturing companies prefer the privacy of their fleets' turbine data for business strategic reasons. The lack of data access impedes the exploitation of opportunities, such as improving data-driven turbine operation and maintenance strategies and reducing downtimes. We present a distributed federated machine learning approach that leaves the data on the wind turbines to preserve the data privacy, as desired by manufacturers, while still enabling fleet-wide learning on those local data. We demonstrate in two case studies that wind turbines which are scarce in representative training data benefit from more accurate fault detection models with federated learning, while no turbine experiences a loss in model performance by participating in the federated learning process. When comparing conventional and federated training processes, the average model training time rises significantly by a factor of up to 14 in the federated training due to increased communication and overhead operations. Thus, model training times might constitute an impediment that needs to be further explored and alleviated in federated learning applications, especially for large wind turbine fleets

Communication-efficient federated learning for digital twin systems of industrial internet of things

With the rapid development and deployment of Industrial Internet of Things technology, it promotes interconnection and edge applications in smart manufacturing. However, challenges remain, such as yet-to-improve communication efficiency and trade-offs between computing power and energy consumption, which limits the application and further development of IIoT technology. This paper proposes the digital twin systems into the IIoT to build model between physical objects and digital virtual systems to optimize the structure of IIoT. And we further introduce federal learning to train the digital twins model and to improve the communication efficiency of IIoT. In this paper, we first establish the digital twins model of IIoT based on industrial scenario. Moreover, to optimize the communication overhead allocation problem, this paper proposes an improved communication-efficient distribution algorithm, which speeds up the training performance of federated model and ensures the performance of industrial system model by changing the update training mode of client and server and allowing some industrial equipment to participate in federated training. This paper simulates the real-word intelligent camera detection to validate the proposed method. Comparing our proposed method with the existing traditional methods, the results show the advantages of the proposed method can improve the communication performance of the training model

On Dynamic Resource Allocation for Blockchain Assisted Federated Learning over Wireless Channels

Blockchain assisted federated learning (BFL) has been intensively studied as a promising technology to process data at the network edge in a distributed manner. In this paper, we focus on BFL over wireless environments with varying channels and energy harvesting at clients. We are interested in proposing dynamic resource allocation (i.e., transmit power, computation frequency for model training and block mining for each client) and client scheduling (DRACS) to maximize the long-term time average (LTA) training data size with an LTA energy consumption constraint. Specifically, we first define the Lyapunov drift by converting the LTA energy consumption to a queue stability constraint. Then, we construct a Lyapunov drift-plus-penalty ratio function to decouple the original stochastic problem into multiple deterministic optimizations along the time line. Our construction is capable of dealing with uneven durations of communication rounds. To make the one-shot deterministic optimization problem of combinatorial fractional form tractable, we next convert the fractional problem into a subtractive-form one by Dinkelbach method, which leads to the asymptotically optimal solution in an iterative way. In addition, the closed-form of the optimal resource allocation and client scheduling is obtained in each iteration with a low complexity. Furthermore, we conduct the performance analysis for the proposed algorithm, and discover that the LTA training data size and energy consumption obey an [$\mathcal{O}(1/V)$, $\mathcal{O}(\sqrt{V})$] trade-off. Our experimental results show that the proposed algorithm can provide both higher learning accuracy and faster convergence with limited time and energy consumption based on the MNIST and Fashion-MNIST datasets