## 1 Introduction  
Networked multi-agent systems (NMASs) have garnered substantial attention in recent decades due to their broad applicability across diverse industries, including unmanned aerial vehicles and unmanned surface vessels [1, 2, 3]. In NMASs, agents are interconnected via communication networks, enabling interaction with the environment and collaboration on complex tasks. However, this interconnectedness also impacts their performance and reliability. Random communication constraints (RCCs) and actuator faults are two common challenges for NMASs. RCCs arise from various factors such as packet loss, communication delays, and intermittent connectivity, which are inherent in wireless communication environments [4, 5, 6]. These constraints can disrupt the flow of information between agents, leading to inaccurate state estimation and suboptimal control decisions. For addressing RCCs, different strategies have been explored. A switched system approach with mode-dependent state feedback controllers was proposed in [7] to improve system performance under RCCs, but it did not explicitly account for actuator faults. A fuzzy fault diagnosis observer was devised in [8] to estimate unknown multiplicative faults under RCCs, yet it did not address network congestion issues. Cloud-based predictive control approaches, such as the one in [9], have been proposed to compensate for RCCs in both feedback and forward channels, but they may introduce additional computational overhead. Additionally, the prolonged operation of NMASs in harsh external environments often results in actuator faults, such as partial failures or bias errors [10; 11]. Such faults can propagate throughout the tightly coupled system, potentially destabilizing the entire network and compromising mission success. Previous research has proposed various fault-tolerant control (FTC) methods for NMASs. Passive FTC approaches, such as that developed in [12], rely on fixed controllers without dynamic fault compensation, which may lack the flexibility to adapt to changing fault conditions. In contrast, active FTC methods, like those in [13; 14], actively estimate and compensate for faults, but they often do not adequately consider the impact of RCCs on fault estimation accuracy. Distributed adaptive control schemes, such as the one introduced in [15], aim to achieve fault-tolerant coordination and collision avoidance, but they may not be optimized for communication efficiency in the presence of network congestion. The large volume of data transfers required for communication among agents can lead to network congestion, consuming the limited bandwidth of the communication network [16]. This congestion not only degrades the system's performance but can also cause instability, as delays in data transmission and control updates become more pronounced [17]. In terms of network congestion, event-triggered control mechanisms have been applied to reduce data transmission [18; 19]. For instance, in [20], a dynamic memory event-triggered mechanism and memory-based distributed fault-tolerant controllers were developed to reduce the number of events triggered. However, these methods may not fully consider the complex interactions between RCCs, actuator faults, and the need for fault-tolerant control. Despite extensive research efforts, addressing these three challenges simultaneously in NMASs remains an open and critical problem [21]. Although many studies have focused on individual aspects of these challenges, there is a notable lack of comprehensive solutions that can effectively mitigate the combined effects of RCCs, actuator faults, and network congestion. The research gap identified in this study is the lack of a unified control method that can simultaneously address RCCs, actuator faults, and network congestion in NMASs. Previous approaches have either focused on individual challenges or have not fully integrated fault-tolerant control with communication efficiency mechanisms.