Federated learning (FL) is proposed as a different approach for distributed learning on the edges while maintaining privacy. Existing FL methods, are mainly focused on learning deep classifying and clustering models, with little consideration offered to the federated reinforcement learning (FRL) task on the edge, a difficult task in which several trained agents track local state and take local actions to train a global learning model without disclosing their local dataset. Several neural network models based on FRL have been presented recently to determine the best method for computation offloading (CO) and resource allocation (RA), particularly in Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC). However, because of the complexity and variety of computational tasks involved in 6G and beyond networks, the FRL algorithms are challenging to apply directly to complicated UAV-assisted MEC scenarios. In this study, we present a generalized FRL approach based on a meta learning technique that incorporates RL models explained by numerous smart devices into a generic model. This research uses a normalized characteristic matrix to divide a complex network into small-scale units and provides a normalized network model for complex network situations based on the FRL meta critic method to determine the CO and RA strategy in a Digital Twin (DT)-enabled UAV-assisted MEC system. Numerical results show that the proposed scheme achieves higher and more reliable overall rewards. Proposed method achieves a 73.15% reduction in reward variance and a 14.23% increase in average rewards over 570 continuous operations.

The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining Quantum Computing with Machine Learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. Quantum Computing, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, quantum computing, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating quantum computing approaches with ML. It also addresses the challenges and limitations of integrating ML and quantum computing in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.

The vehicle road cooperation system (VRCS) facilitates vehicle-to-vehicle (V2V) communication for future vehicle usage in sixth generation (6G) networks. The implementation of the 6G network has made it possible for V2V communication to enhance network density, optimize transmission mode selection, and offer connectivity between vehicles while guaranteeing quality of service (QoS). However, there are inherent challenges such as limited bandwidth, diverse QoS requirements, interference, power constraints associated with resource allocation and mode selection in V2V and vehicle-to-everything (V2X) communication. In this article, we jointly optimized the mode selection and resource allocation problems in VRCS by using simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS). The proposed model utilizes quantum federated reinforcement learning (QFRL) based Augmented Intelligence algorithms within the STAR-RIS VRCS framework. The proposed QFRL algorithm is a promising solution for advanced decision-making, automation to improve traffic flow, reduces traffic congestion, and improve safety in the STAR-RIS assisted VRCS. Additionally, by leveraging the unique processing advantage of quantum computing will make the VRCS more capable of handling the enormous amount of real-time data that IoT devices send, which is necessary for the intelligent services it offers. The proposed model QFRL based STAR-RIS assisted VRCS approach maximizes vehicle-to-infrastructure (V2I) user capacity while meeting the reliability requirement of V2V pairs. Finally, the simulation results prove the superiority of the QFRL algorithm against baseline schemes like quantum federated learning (QFL), federated reinforcement learning (FRL) and federated learning (FL) algorithms for V2V pairs. Furthermore, the performance evaluation findings indicate that the proposed STAR-RIS assisted QFRL algorithm performs 20.5%, 32.2%, and 46.7% better than QFL, FRL, and FL.