IoT data exchange plays a vital role in supporting various applications and services with massive IoT devices. However, the existence of malicious devices threatens the integrity and reliability of the exchanged data. Trust management has been used to mitigate the impact of malicious devices in centralized and decentralized architectures. However, most of these traditional trust management systems bear computation, storage, and communication challenges. In this study, we propose a semi-centralized trust management system architecture based on blockchain in both single and multiple domains. The IoT devices are centralized organized by cloud servers who coordinately sustain a rating data ledger within each domain based the proposed rotation based consensus protocol in a decentralized manner to support cross-domain data exchange. A computational trust model is proposed by aggregating the direct and indirect trust information, where we elaborately design decay function, recommendation credibility and adaptable weights so as to calculate the trust value of dynamic malicious devices. Finally, we evaluate the proposed system model in various situations through simulation based experiments and compare it with two classical models in the literature. The experimental results demonstrate the effectiveness of the proposed trust model in identifying malicious devices and mitigating the influence of malicious devices.

Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated Learning (FL) with the exceptional parallel mechanism and exponential speed enhancements characteristic of quantum computing. While this innovative fusion has garnered considerable attention, a notable gap in current research is the tendency to consider traditional FL and its quantum-enhanced counterpart, QFL, in isolation. This approach often overlooks the critical role of Quantum Machine Learning (QML) in effectively bridging these two domains. Recognizing this, there emerges a pressing need for a comprehensive survey that encompasses the entire spectrum of FL paradigms, from foundational FL concepts to the cutting-edge developments in QFL. Our survey aims to address this need by providing an in-depth exploration of the various facets of FL paradigms, ultimately leading to a thorough understanding of Quantum Federated Learning. We start by emphasizing the driving factors and prevalent research topics related to FL. To develop a more efficient, robust, and precise computing paradigm, we investigate the current solutions that address the concerns of heterogeneity, privacy, security, and evaluation in FL. After that, we explore the possibility of improving the computational efficiency of ML methods by leveraging the computational capabilities of quantum computers. In particular, we discuss the frameworks, evaluation, and applications for QML. Following that, we detail the integration of quantum computing technologies with standard FL, aiming to bolster computational performance and mitigate security and privacy risks. The potential solutions to improve the efficiency (i.e., slimming mechanism) and respect the privacy and security (i.e., quantum key distribution) for QFL are explored. Finally, we outline some critical future directions towards unlocking the full potential of QFL in distributed machine learning.

With the rapid development of healthcare-based cyber physical systems (CPSs), more and more healthcare data are collected from clinical institutions or hospitals. Due to the private and fragmented nature, healthcare data is quite suitable to be processed by federated learning (FL) paradigm, where a shared global model is aggregated by a central server while keeping the sensitive healthcare data in local hospitals. However, there are two practical issues: (1) the centralized FL server may not honestly aggregate the final model, and (2) the FL participants lack incentive to contribute their efforts. In this study, we propose a blockchain-empowered FL framework for healthcare-based CPSs. A distributed ledger is maintained by a task agreement committee which is composed by the representators of the hospitals who execute FL tasks. A secure FL task model training-based consensus process is proposed to generate consistent blocks. Furthermore, an contribution point-based incentive mechanism is designed to fairly reward FL participators for contributing their local data. We evaluate the proposed system base on real healthcare data and the numerical results demonstrate its effectiveness in achieving FL model aggregation truthfulness and efficiency in providing incentives for FL participants.

In the context of the social Internet of vehicles (SIoV), constructing reliable social relationships between dynamic and distributed entities is a challenging research problem. Rating-based reputation systems have been widely applied to assist human users in evaluating the honesty of target entities. However, the ratings in SIoV expose user privacy, including behavior, location, etc., which are required to be protected properly. Meanwhile, the blockchain technology with its distributed paradigm is potentially employed to protect information privacy. In this study, we propose the design of a blockchain-enabled reputation system named “VRepChain” for SIoV by especially considering the rating privacy issue. In our design, the ratings' privacy is strongly preserved in the processes of transmission and storage. The reputation of a vehicle is constructed based on the ratings with the agreement of the rating providers, ensuring the ratings are never abused by any other unauthorized entities during the usage process. Through experiments, the proposed system is demonstrated to improve the effectiveness of vehicles in terms of arriving at their destinations in a faster speed. Furthermore, the effectiveness of the constructed reputation model with untruthful ratings is extensively examined, showing its robustness and practicality in realistic applications.