Related papers: Fast Networks for High-Performance Distributed Tru…
In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for…
Data spaces are emerging as decentralised infrastructures that enable sovereign, secure, and trustworthy data exchange among multiple participants. To achieve semantic interoperability within these environments, the use of semantic web…
Data sharing is the fuel of the galloping artificial intelligence economy, providing diverse datasets for training robust models. Trust between data providers and data consumers is widely considered one of the most important factors for…
Application Service Providers (ASPs) obtaining resources from multiple clouds have to contend with different management and control platforms employed by the cloud service providers (CSPs) and network service providers (NSP). Distributing…
Recent advances in data-generating techniques led to an explosive growth of geo-spatiotemporal data. In domains such as hydrology, ecology, and transportation, interpreting the complex underlying patterns of spatiotemporal interactions with…
Wide scale interest and adoption of Internet of Things (IoT) technologies is fuelling innovation in the way individuals and even machines can interact to exchange knowledge. One area of particular interest is that of analytics. Ever…
Internet of Things (IoT) networks for smart sensor nodes in the next generation of smart wireless sensing systems require a distributed security scheme to prevent the passive (eavesdropping) or active (jamming and interference) attacks from…
In this paper, we propose and evaluate a distributed protocol to manage trust diffusion in ad hoc networks. In this protocol, each node i maintains a \trust value" about an other node j which is computed both as a result of the exchanges…
Applications are increasingly written as dynamic workflows underpinned by an execution framework that manages asynchronous computations across distributed hardware. However, execution frameworks typically offer one-size-fits-all solutions…
Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning…
The growth in the number of devices connected to the Internet of Things (IoT) poses major challenges in security. The integrity and trustworthiness of data and data analytics are increasingly important concerns in IoT applications. These…
Rapid urbanization burdens city infrastructure and creates the need for local governments to maximize the usage of resources to serve its citizens. Smart city projects aim to alleviate the urbanization problem by deploying a vast amount of…
Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles to support diverse vertical applications by connecting heterogenous devices, machines and…
Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era.…
Graph Neural Networks (GNNs) have become popular across a diverse set of tasks in exploring structural relationships between entities. However, due to the highly connected structure of the datasets, distributed training of GNNs on…
This paper takes a parallel learning approach for robust and transparent AI. A deep neural network is trained in parallel on multiple tasks, where each task is trained only on a subset of the network resources. Each subset consists of…
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by…
Distributed training is a novel approach to accelerate Deep Neural Networks (DNN) training, but common training libraries fall short of addressing the distributed cases with heterogeneous processors or the cases where the processing nodes…
This paper investigates the impact of artificial intelligence integration on remote operations, emphasising its influence on both distributed and team cognition. As remote operations increasingly rely on digital interfaces, sensors, and…
Distributed computing often gives rise to complex concurrent and interacting activities. In some cases several concurrent activities may be working together, i.e. cooperating, to solve a given problem; in other cases, the activities may be…