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

Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets by enabling the training of a model across nodes holding their data samples locally. To achieve…

Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes disadvantages of today's centralized infrastructures, such as, among others, high cloud server maintenance costs,…

Blockchain was always associated with Bitcoin, cryptocurrencies, and digital asset trading. However, its benefits are far beyond that. It supports technologies like the Internet-of-Things (IoT) to pave the way for futuristic smart…

Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in…

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with…

With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible. Recent efforts toimprove users'…

Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the…

The rapid expansion of data worldwide invites the need for more distributed solutions in order to apply machine learning on a much wider scale. The resultant distributed learning systems can have various degrees of centralization. In this…

Blockchain has recently been able to draw wider attention throughout the research community. Since its emergence, the world has seen the mind-blowing expansion of this new technology, which was initially developed as a pawn of digital…

The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently, artificial intelligence (AI) has been widely utilized for…

Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security…

In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly…

Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it becomes more difficult to implement and manage a blockchain…

Integrating blockchain technology with the Internet of Things offers transformative possibilities for enhancing network security and privacy in the contemporary digital landscape, where interconnected devices and expansive networks are…

The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical.…

The Internet of Vehicles (IoV) is flourishing and offers various applications relating to road safety, traffic and fuel efficiency, and infotainment. Dealing with security and privacy threats and managing the trust (detecting malicious and…

Implementing existing federated learning in massive Internet of Things (IoT) networks faces critical challenges such as imbalanced and statistically heterogeneous data and device diversity. To this end, we propose a semi-federated learning…

Federated Learning (FL) has recently emerged as a collaborative learning paradigm that can train a global model among distributed participants without raw data exchange to satisfy varying requirements. However, there remain several…

Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training. Until now, the majority of research has…

Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML…