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Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that…

Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks. Nevertheless, people are concerned about whether…

The knowledge, embodied in machine learning models for intelligent systems, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labelling, network training, and fine-tuning of models.…

Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers…

With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel…

Blockchain or Distributed Ledger Technology is a disruptive technology that provides the infrastructure for developing decentralized applications enabling the implementation of novel business models even in traditionally centralized…

Blockchain has proven to be an emerging technology in the digital world, changing the way everyone thinks about data security and bringing efficiency to several industries. It has already been applied to a wide range of applications, from…

Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence,…

In the past decade, blockchain has shown a promising vision greatly to build the trust without any powerful third party in a secure, decentralized and salable manner. However, due to the wide application and future development from…

Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on…

A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational…

To address the large amount of energy wasted by blockchains, we propose a decentralized consensus protocol for blockchains in which the computation can be used to search for good approximate solutions to any optimization problem. Our…

Edge computing brings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept of edge federation has recently emerged as a key mechanism for dynamic resources and services…

Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a central server for global aggregation. The practical…

As the application of federated learning becomes increasingly widespread, the issue of imbalanced training data distribution has emerged as a significant challenge. Federated learning utilizes local data stored on different training clients…

Decentralized Federated learning is a distributed edge intelligence framework by exchanging parameter updates instead of training data among participators, in order to retrain or fine-tune deep learning models for mobile intelligent…

Blockchain-based federated learning has gained significant interest over the last few years with the increasing concern for data privacy, advances in machine learning, and blockchain innovation. However, gaps in security and scalability…

Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility usually lead to challenges in message…

Decentralized techniques are becoming crucial and ubiquitous with the rapid advancement of distributed ledger technologies such as the blockchain. Numerous decentralized systems have been developed to address security and privacy issues…

Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks. It plays a key role in various areas, such as fraud detection,…