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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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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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 […]
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An emergence of attention and regulations on consumer privacy can be observed over the recent years with the ubiquitous availability of IoT systems handling personal data. Federated Learning (FL) arises as a privacy-preserved Machine Learning (ML) technique where data can be kept private within these devices without transmitting to third parties. Yet, many privacy attacks […]
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A wide adoption of Artificial Intelligence (AI) can be observed in recent years over networking to provide zero-touch, full autonomy of services towards the next generation Beyond 5G (B5G)/6G. However, AI-driven attacks on these services are a major concern in reaching the full potential of this future vision. Identifying how resilient the AI models are […]
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Federated learning (FL) is an intriguing approach to privacy-preserving collaborative learning. Decentralised FL is achieving increased favour for investigation due to the mitigation of vulnerability for a single point of failure and more controllability for end users over their models. However, many existing decentralised FL systems face limitations, such as privacy concerns, latency in aggregation, […]
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Federated Learning (FL) is an emerging privacy-preserved distributed Machine Learning (ML) technique where multiple clients can contribute to training an ML model without sharing private data. Even though FL offers a certain level of privacy by design, recent works show that FL is vulnerable to numerous privacy attacks. One of the key features of FL […]
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Metaverse is a key emerging digital transformation concept for the next generation of cyberspace. It is expected to create a self-sustaining virtual ecosystem of fully immersive, real-time experiences with numerous opportunities for general users and industries to interact with the world. With the introduction of 6G networks and enabling technologies, the metaverse will achieve its […]
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AIn the current 5th Generation (5G) networkingparadigm, the enforcement of Service Level Agreements (SLAs)is a non-trivial measure to ensure the scope and the qual-ity of services and standards between tenants and serviceproviders (SPs). On top of this, Secure Service Level Agree-ments (SSLA) are introduced to ensure that SPs deliver themost critical and required security-related standards […]
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