How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems

Multi-agent cyberphysical systems enable new capabilities in efficiency, resilience, and security. The unique characteristics of these systems prompt a reevaluation of their security concepts, including their vulnerabilities, and mechanisms to mitigate these vulnerabilities. This survey paper examines how advancement in wireless networking, coupled with the sensing and computing in cyberphysical systems, can foster novel security capabilities. This study delves into three main themes related to securing multi-agent cyberphysical systems. First, we discuss the threats that are particularly relevant to multi-agent cyberphysical systems given the potential lack of trust between agents. Second, we present prospects for sensing, contextual awareness, and authentication, enabling the inference and measurement of ``inter-agent trust" for these systems. Third, we elaborate on the application of quantifiable trust notions to enable ``resilient coordination," where ``resilient" signifies sustained functionality amid attacks on multiagent cyberphysical systems. We refer to the capability of cyberphysical systems to self-organize, and coordinate to achieve a task as autonomy. This survey unveils the cyberphysical character of future interconnected systems as a pivotal catalyst for realizing robust, trust-centered autonomy in tomorrow's world.

Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy

This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server. To mitigate the impact of intermittent links, nodes can collaborate with their neighbors to compute local consensus which they forward to the central server. In such a setup, the communications between any pair of nodes must satisfy local differential privacy constraints. We study the tradeoff between collaborative relaying and privacy leakage due to the additional data sharing among nodes and, subsequently, propose a novel differentially private collaborative algorithm for DME to achieve the optimal tradeoff. Finally, we present numerical simulations to substantiate our theoretical findings.

Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

Enhancing resilience in distributed networks in the face of malicious agents is an important problem for which many key theoretical results and applications require further development and characterization. This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's dynamic is influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, we present numerical results that validate the analytical convergence guarantees we present in this paper even when the malicious agents compose the majority of agents in the network.

Semi-Decentralized Federated Learning with Collaborative Relaying

We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every communication round to the PS, each client computes a local consensus of the updates from its neighboring clients and eventually transmits a weighted average of its own update and those of its neighbors to the PS. We appropriately optimize these averaging weights to ensure that the global update at the PS is unbiased and to reduce the variance of the global update at the PS, consequently improving the rate of convergence. Numerical simulations substantiate our theoretical claims and demonstrate settings with intermittent connectivity between the clients and the PS, where our proposed algorithm shows an improved convergence rate and accuracy in comparison with the federated averaging algorithm.

Composite IG/FTR Channel Performance in Wireless Communication Systems

We present a composite wireless fading model encompassing multipath fading and shadowing based on fluctuating two-ray (FTR) fading and inverse gamma (IG) shadowing. We first determine an alternative framework for the statistical characterization and performance evaluation of the FTR fading model, which is based on the fact that the FTR fading distribution can be described as an underlying Rician Shadowed (RS) distribution with continuously varying parameter Kr (ratio of specular to diffuse components). We demonstrate that this new formulation permits to obtain a closed-form expression of the generalized moment generating function (GMGF) of the FTR model, from which the PDF and CDF of the composite IG/FTR model can be obtained in closed-form. The exact and asymptotic outage probability of the IG/FTR model are analyzed and verified by Monte Carlo simulations.

Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying

Intermittent client connectivity is one of the major challenges in centralized federated edge learning frameworks. Intermittently failing uplinks to the central parameter server (PS) can induce a large generalization gap in performance especially when the data distribution among the clients exhibits heterogeneity. In this work, to mitigate communication blockages between clients and the central PS, we introduce the concept of knowledge relaying wherein the successfully participating clients collaborate in relaying their neighbors' local updates to a central parameter server (PS) in order to boost the participation of clients with intermittently failing connectivity. We propose a collaborative relaying based semi-decentralized federated edge learning framework where at every communication round each client first computes a local consensus of the updates from its neighboring clients and eventually transmits a weighted average of its own update and those of its neighbors to the PS. We appropriately optimize these averaging weights to reduce the variance of the global update at the PS while ensuring that the global update is unbiased, consequently improving the convergence rate. Finally, by conducting experiments on CIFAR-10 dataset we validate our theoretical results and demonstrate that our proposed scheme is superior to Federated averaging benchmark especially when data distribution among clients is non-iid.

Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms

We consider the problem of quantizing a linear model learned from measurements $\mathbf{X} = \mathbf{W}\boldsymbol{\theta} + \mathbf{v}$. The model is constrained to be representable using only $dB$-bits, where $B \in (0, \infty)$ is a pre-specified budget and $d$ is the dimension of the model. We derive an information-theoretic lower bound for the minimax risk under this setting and show that it is tight with a matching upper bound. This upper bound is achieved using randomized embedding based algorithms. We propose randomized Hadamard embeddings that are computationally efficient while performing near-optimally. We also show that our method and upper-bounds can be extended for two-layer ReLU neural networks. Numerical simulations validate our theoretical claims.

Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors may collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster. The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center. Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme. Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors' and clusters' detection quality, heterogeneity in their communication opportunities, and non-convexity of the loss function. Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.

Alternative Formulations for the Fluctuating Two-Ray Fading Model

We present two alternative formulations for the distribution of the fluctuating two-ray (FTR) fading model, which simplify its statistical characterization and subsequent use for performance evaluation. New expressions for the probability density function (PDF) and cumulative distribution function of the FTR model are obtained based on the observation that the FTR fading distribution is described, for arbitrary $m$, as an underlying Rician Shadowed (RS) distribution with continuously varying parameter $K$, while for the special case of $m$ being an integer, the FTR fading model is described in terms of a finite number of underlying squared Nakagami-$m$ distributions. It is shown that the chief statistics and any performance metric that are computed by averaging over the PDF of the FTR fading model can be expressed in terms of a finite-range integral over the corresponding statistic or performance metric for the RS (for arbitrary $m$) or the Nakagami-$m$ (for integer $m$) fading models, which have a simpler analytical characterization than the FTR model and for which many results are available in closed-form. New expressions for some Laplace-domain statistics of interest are also obtained; these are used to exemplify the practical relevance of this new formulation for performance analysis.