相关论文: A Blockchain Solution for Collaborative Machine Le…

Blockchain-based Federated Learning (FL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still…

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive…

In the age of cloud computing, data privacy protection has become a major challenge, especially when sharing sensitive data across cloud environments. However, how to optimize collaboration across cloud environments remains an unresolved…

The convergence of blockchain and the Internet of Things (IoT) enables secure, decentralised, and verifiable data exchange across distributed smart environments. However, traditional blockchain frameworks suffer from inherent scalability…

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server…

In Federated Deep Learning (FDL), multiple local enterprises are allowed to train a model jointly. Then, they submit their local updates to the central server, and the server aggregates the updates to create a global model. However, trained…

Blockchain (BC), a by-product of Bitcoin cryptocurrency, has gained immense and wide scale popularity for its applicability in various diverse domains - especially in multifaceted non-monetary systems. By adopting cryptographic techniques…

Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequent uplink transmission. While pre-trained models are…

Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application…

This paper presents a novel reference architecture for blockchain-enabled federated learning (BCFL), a state-of-the-art approach that amalgamates the strengths of federated learning and blockchain technology.We define smart contract…

In the era of the Internet of Things (IoT), massive computing devices surrounding us operate and interact with each other to provide several significant services in industries, medical as well as in daily life activities at home, office,…

In this paper, we develop a new framework called blockchain-based Radio Frequency (RF)-powered backscatter cognitive radio network. In the framework, IoT devices as secondary transmitters transmit their sensing data to a secondary gateway…

The Internet of Things (IoT) is undergoing rapid growth in the IT industry, but, it continues to be associated with several security and privacy concerns as a result of its massive scale, decentralised topology, and resource-constrained…

With the increasing importance of machine learning, the privacy and security of training data have become critical. Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant…

Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where…

The internet of things (IoT) and other emerging ubiquitous technologies are supporting the rapid spread of smart systems, which has underlined the need for safe, open, and decentralized data storage solutions. With its inherent…

With the rapid development of low-cost consumer electronics and cloud computing, Internet-of-Things (IoT) devices are widely adopted for supporting next-generation distributed systems such as smart cities and industrial control systems. IoT…

The rise of space AI is reshaping government and industry through applications such as disaster detection, border surveillance, and climate monitoring, powered by massive data from commercial and governmental low Earth orbit (LEO)…

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed. However, it is of great challenge for current FL frameworks to improve communication performance and maintain the…

The ongoing deployment of the Internet of Things (IoT)-based smart applications is spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there…