Related papers: BUDAMAF: Data Management in Cloud Federations

Cloud computing allows shared computer and storage facilities to be used by a multitude of clients. While cloud management is centralized, the information resides in the cloud and information sharing can be implemented via off-the-shelf…

The rapid adoption of AI systems presents enterprises with a dual challenge: accelerating innovation while ensuring responsible governance. Current AI governance approaches suffer from fragmentation, with risk management frameworks that…

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and…

Ensuring data correctness over partitioned distributed database systems is a classical problem. Classical solutions proposed to solve this problem are mainly adopting locking or blocking techniques. These techniques are not suitable for…

Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating costs while also…

The digital identity problem is a complex one in large part because it involves personal data, the algorithms which compute reputations on the data and the management of the identifiers that are linked to personal data. The reality of today…

Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. However, the presence of statistical…

Modern organizations manage their data with a wide variety of specialized cloud database engines (e.g., Aurora, BigQuery, etc.). However, designing and managing such infrastructures is hard. Developers must consider many possible designs…

In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Federated Computing (FC) addresses these concerns by enabling…

Federated Learning (FL) is a distributed Machine Learning (ML) technique that can benefit from cloud environments while preserving data privacy. We propose Multi-FedLS, a framework that manages multi-cloud resources, reducing execution time…

With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace…

As demand for more storage and processing power increases rapidly, cloud services in general are becoming more ubiquitous and popular. This, in turn, is increasing the need for developing highly sophisticated mechanisms and governance to…

Federated transaction management has long been used as a method to virtually integrate multiple databases from a transactional perspective, ensuring consistency across the databases. Modern approaches manage transactions on top of a…

Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is particularly appealing because of its ability to accommodate…

In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing…

Cloud computing is a complex infrastructure of software, hardware, processing, and storage that is available as a service. Cloud computing offers immediate access to large numbers of the world's most sophisticated supercomputers and their…

The pervasive use of hybrid cloud computing models has changed enterprise as well as Information Technology services infrastructure by giving businesses simple and cost-effective options of combining on-premise IT equipment with public…

Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, often under different administrative domains. Fluid Computing has emerged as a…

Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks. Although many FL algorithms have been proposed, few of them have considered the matrix…

Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations.…