In response to the challenges of high centralization, low node voting enthusiasm, and vulnerability to manipulation in committee-based blockchain systems, this paper proposes a decentralized node management method based on a multi-level committee structure. The method introduces a comprehensive node assessment model for preliminary screening of all network nodes, and incorporates a point decay algorithm to dynamically elect block-producing nodes, thereby ensuring the system's decentralized nature. Finally, a reward and punishment incentive mechanism is introduced to encourage nodes to maintain active behavior within the blockchain system. Experiments show that the multi-level architecture and the proposed consensus algorithm improve the decentralization level of the blockchain system compared to similar algorithms, and achieve better performance in terms of resistance to manipulation, block production speed, node voting enthusiasm, and the speed of malicious node removal.

In the 5G era, ultra-dense networks (UDNs) face significant challenges in cost and energy efficiency optimization. This study proposes a network planning and design approachthat combines unmanned aerial vehicles (UAVs) and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). An ultra-dense network model is constructed, incorporating macro base station, user terminals, and UAV-carried STAR-RIS modules. The soft actor-critic (SAC) algorithm is employed to optimize network parameters with the goal of maximizing system efficiency while meeting communication rate requirement. Rate and power models are developed and an objective function of energy and cost efficiency is formulated. The SAC algorithm is adopted to solve the optimization problem and determine the optimal network configuration under constraints of power, rate, and cost. Simulation results indicate that the proposed approach effectively enhances network energy and cost efficiency, thereby reducing energy consumption and deployment cost of the system.

In scenarios where client data are non-independent and identically distributed (Non-IID), this paper proposes a communication-efficient personalized federated learning algorithm based on Non-IID data to provide personalized and efficient communication solutions for clients. Specifically, to leverage the knowledge among similar clients and retain personalized information of local clients, we develop a personalized federated learning algorithm that combines hierarchical modeling with clustering ideas. Furthermore, to address the issue of high communication overhead, we design a selective model aggregation strategy. The central server evaluates the similarity between the client data distribution and the global data distribution using maximum mean discrepancy. Based on this similarity, the server computes a priority score for each client and selects those with higher scores for communication. This strategy effectively reduces the cumulative communication rounds between clients and the central server, thereby improving communication efficiency and accelerating model convergence. Experimental results demonstrate that compared with existing representative works, the proposed algorithm reduces the cumulative commu nication rounds by over 50% while maintaining high accuracy.