The study investigates the bipartite consensus of linear multi-agent systems under sequential scaling attacks based on observers. First, since the attack degree of the sequential scaling attack is unknown, observers are designed to estimate the real states of the agents to reduce the impact on the stability of the error system. Second, the attack signals are injected into all controller-to-actuator channels throughout the network. and a distributed controller based on observer's state-estimates is proposed for the linear multi-agent system, which can effectively avoid the use of information from the system itself. Finally, the Lyapunov function proves that the designed controller can enable the linear multi-agent system subject to sequential scaling attacks to achieve bipartite consensus. And numerical simulations validate the theoretical results.

Unlike outdoor environments, indoor spaces exhibit complex electromagnetic conditions that complicate the localization of targets using wireless signals. Based on the Fresnel propagation model, this paper studies the propagation law of Wi-Fi signals in a rich multi-path environment. First, to address the phase offset in channel state information caused by hardware, a correction method based on least squares regression analysis is proposed, and environmental interference signals are eliminated by spectrum analysis. Second, the target position is estimated by using multiple groups of signal links, and the estimated result is matched with the reference position to reconstruct the target motion path. Experimental results show that the theoretical basis of this method is consistent with the actual movement patterns of the target, achieving a positioning error of about 11 cm without data training. At this error level, this method can distinguish adjacent targets in lateral or longitudinal proximity, thereby reducing misidentification. Compared with the existing deep learning-based methods, the proposed method reduces the positioning error by 50.4% with less training data, and by 5% with ample training data.

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.