39 citations · 130 across the 45 of their papers we have counts for
11 papers · 1 filter
Byzantine Resilient Distributed Multi-Task Learning
Jiani Li, Waseem Abbas, Xenofon Koutsoukos
Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models sim…
Interplay Between Resilience and Accuracy in Resilient Vector Consensus in Multi-Agent Networks
Waseem Abbas, Mudassir Shabbir, Jiani Li +1
In this paper, we study the relationship between resilience and accuracy in the resilient distributed multi-dimensional consensus problem. We consider a network of agents, each of…
Strong Structural Controllability of Diffusively Coupled Networks: Comparison of Bounds Based on Distances and Zero Forcing
Yasin Yazicioglu, Mudassir Shabbir, Waseem Abbas +1
We study the strong structural controllability (SSC) of diffusively coupled networks, where the external control inputs are injected to only some nodes, namely the leaders. For suc…
Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems
Dimitrios Boursinos, Xenofon Koutsoukos
Cyber-physical systems (CPS) can benefit by the use of learning enabled components (LECs) such as deep neural networks (DNNs) for perception and decision making tasks. However, DNN…
Resilient Distributed Diffusion for Multi-task Estimation
Jiani Li, Xenofon Koutsoukos
Distributed diffusion is a powerful algorithm for multi-task state estimation which enables networked agents to interact with neighbors to process input data and diffuse informatio…
Resilient Distributed Diffusion in Networks with Adversaries
Jiani Li, Waseem Abbas, Xenofon Koutsoukos
In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states…
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression
Feiyang Cai, Jiani Li, Xenofon Koutsoukos
Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level o…
Improving Network Robustness through Edge Augmentation While Preserving Strong Structural Controllability
Waseem Abbas, Mudassir Shabbir, Hassan Jaleel +1
In this paper, we consider a network of agents with Laplacian dynamics, and study the problem of improving network robustness by adding a maximum number of edges within the network…
Resilient Distributed Vector Consensus Using Centerpoints
Waseem Abbas, Mudassir Shabbir, Jiani Li +1
In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms. A common approach to ac…
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems
Feiyang Cai, Xenofon Koutsoukos
Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep…
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components
Dimitrios Boursinos, Xenofon Koutsoukos
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, they may introduce new types of hazards that can have disast…