# On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective

arXiv: https://arxiv.org/abs/2104.07853 · HTML: https://mlsanity.com/paper/2104.07853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [Trust-Aware Service Function Chain Embedding: A Path-Based Approach](https://mlsanity.com/paper/2009.07343/) (2020)
- [Joint Mobility-Aware UAV Placement and Routing in Multi-Hop UAV Relaying Systems](https://mlsanity.com/paper/2009.14446/) (2020)

## Built on by (most cited first, 1 of 1)

- [HASHTAG: Hash Signatures for Online Detection of Fault-Injection Attacks on Deep Neural Networks](https://mlsanity.com/paper/2111.01932/) (2021)

## Abstract (arXiv)

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.