The BlockDataTrust Benchmark Dataset (BlockDataTrust) is a comprehensive synthetic dataset developed to support research on data trustworthiness in blockchain-integrated systems. It was created as part of ongoing research at University College Dublin, specifically to address the growing need for structured, machine learning–ready data to evaluate trust in decentralized environments. Unlike existing blockchain datasets that typically focus […]
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With a plethora of new connections, features, and services introduced, the 5th generation (5G) wireless technology reflects the development of mobile communication networks and is here to stay for the next decade. The multitude of services and technologies that 5G incorporates have made modern communication networks very complex and sophisticated in nature. This complexity along […]
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The Netslab-5G Open RAN Intrusion Detection Dataset (Netslab-5GORAN-IDD) is a real-world dataset collected using a physical 5G Open RAN testbed at University College Dublin. It includes both higher-layer network traffic data and lower-layer radio and device-level metrics, making it one of the few comprehensive datasets available for research in Open RAN security. This dataset was […]
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The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for […]
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Large Language Models (LLMs) have revolutionized natural language processing, but deploying them in resource-constrained environments and privacy-sensitive domains remains challenging. This paper introduces the Distributed Large Language Model (DisLLM), a novel distributed learning framework that addresses privacy preservation and computational efficiency issues in LLM fine-tuning and inference. DisLLM leverages the Splitfed Learning (SFL) approach, combining […]
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The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such asultra-high data rates, dense connectivity, and high scalability. It opens many possibilities for a new generation of services driven by Artificial Intelligence (AI) and billions of interconnected smart devices. However, with this expected massive upgrade, the privacy of people, organizations, […]
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With the rapid progression of communication and localisation of big data over billions of devices, distributed Machine Learning (ML) techniques are emerging to cater for the development of Artificial Intelligence (AI)-based services in a distributed manner. Federated Learning (FL) is such an innovative approach to achieve a privacy-preserved AI that facilitates ML model sharing and […]
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As 6G networks introduce increasingly diverse and complex applications, network slicing is a key enabling technology for partitioning network resources to meet these dynamic demands. However, efficiently managing and allocating these finite resources has become vital. This necessity drives the adoption of an open marketplace model. To address the business and technical complexities associated with […]
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The shift from fifth generation (5G) to sixth generation (6G) networks is anticipated to significantly advance network slicing. This progress is driven by the growing demand for next-generation applications and services. However, these advancements must be managed within the constraints of limited resources. This evolution opens up opportunities for resource sharing through emerging marketplaces, yet […]
Read MoreEnergy blockchain applications are becoming inevitable with the transformation of electricity distributionnetworks into the decentralized Smart Grid 2.0 architecture. The scalability of the blockchain platform plays a key role in catering to the increasing number of nodes connected due to consumerturned-prosumers being integrated into the distribution grid in a distributed manner. Hence, this study aims […]
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