Proactive caching in 6 G cloud-edge collaboration scenarios, intelligently and periodically updating the cached contents, can either alleviate the traffic congestion of backhaul link and edge cooperative link or bring multimedia services to mobile users. To further improve the network performance of 6 G cloud-edge, we consider the issue of multi-objective joint optimization,i.e., maximizing edge hit ratio while minimizing content access latency and traffic cost. To solve this complex problem, we focus on the distributed deep reinforcement learning (DRL)-based method for proactive caching, including content prediction and content decision-making. Specifically, since the prior information of user requests is seldom available practically in the current time period, a novel method named temporal convolution sequence network (TCSN) based on the temporal convolution network (TCN) and attention model is used to improve the accuracy of content prediction. Furthermore, according to the value of content prediction, the distributional deep Q network (DDQN) seeks to build a distribution model on returns to optimize the policy of content decision-making. The generative adversarial network (GAN) is adapted in a distributed fashion, emphasizing learning the data distribution and generating compelling data across multiple nodes. In addition, the prioritized experience replay (PER) is helpful to learn from the mosteffectivesample. So we propose a multivariate fusion algorithm called PG-DDQN. Finally, faced with such a complex scenario, a distributed learning architecture,i.e., multi-agent learning architecture is efficiently used to learn DRL-based methods in a manner of centralized training and distributed inference. The experiments prove that our proposal achieves satisfactory performance in terms of edge hit ratio, traffic cost and content access latency.

Mixed-precision tuning is a valuable approach for striking a balance between the accuracy and performance of floating-point computations. However, selecting suitable configurations from numerous mixed-precision configurations can be challenging. The discrete and finite nature of floating-point numbers introduces unavoidable rounding errors that are difficult to predict. To address these challenges, we propose an accuracy gain evaluation function for expressions. This evaluation function quantifies the extent of accuracy improvement achieved by adjusting the precision of the operations within the expression. This is achieved through a thorough examination of the impact of each operation's condition number and implementation error on the final error. Based on this evaluation function, we present AMPT, a mixed-precision tuning method specifically for expressions. AMPT not only identifies the optimal mixed-precision configuration but also automatically generates the corresponding code. Experimental results demonstrate that AMPT accurately assesses the accuracy gains of different mixed-precision configurations, and the mixed-precision code generated by AMPT can achieve low-error computation of expressions with low performance overhead.

We study the security issue of distributed state estimation under data integrity attacks over wireless sensor networks. We design a detector based on statistical learning to judge the compromised estimate sent from the neighboring sensors. To obtain the best estimation performances, we find an optimal estimator for sensors equipped with the malicious data detector, and find a sufficient condition to ensure the stability of the trace of estimation error covariances (EECs). In addition, we explore the relationship between the steady-state EEC and the parameters of the detector. Finally, by numerical simulations, we show the performances of several typical detectors proposed in the existing works, and verify the influence of the detector parameters on the estimation performances.