  With the increasing scale, complexity, and heterogeneity of the next
generation networked systems, seamless control, management, and security of
such systems becomes increasingly challenging. Many diverse applications have
driven interest in networked systems, including large-scale distributed
learning, multi-agent optimization, 5G service provisioning, and network
slicing, etc. In this paper, we propose trust as a measure to evaluate the
status of network agents and improve the decision-making process. We interpret
trust as a relation among entities that participate in various protocols. Trust
relations are based on evidence created by the interactions of entities within
a protocol and may be a composite of multiple metrics such as availability,
reliability, resilience, etc. depending on application context. We first
elaborate on the importance of trust as a metric and then present a
mathematical framework for trust computation and aggregation within a network.
Then we show in practice, how trust can be integrated into network
decision-making processes by presenting two examples. In the first example, we
show how utilizing the trust evidence can improve the performance and the
security of Federated Learning. Second, we show how a 5G network resource
provisioning framework can be improved when augmented with a trust-aware
decision-making scheme. We verify the validity of our trust-based approach
through simulations. Finally, we explain the challenges associated with
aggregating the trust evidence and briefly explain our ideas to tackle them.
