arXiv subjects

Yuwen Ma

Publications and source records attributed to Yuwen Ma.

4 recordsLinked to original sources

arXiv2026

To What Extent Can Inherent Communication Noise Guarantee Privacy in Distributed Cooperative Control?

This paper proposes a differentially private distributed cooperative control scheme for multi-agent systems (MAS). Unlike conventional approaches that actively inject artificial noise for privacy protection, this work investigates whether inherent communication noise can itself serve as a natural privacy mechanism. A physically motivated communication-noise model is developed for mobile MAS by incorporating transmitter perturbation, receiver noise, path-loss attenuation, and log-normal shadowing. The resulting effective noise variance depends on inter-agent state differences, thereby capturing the distance-dependent signal perturbation arising in practice. Based on this model, a distributed finite-horizon Linear Quadratic Regulator (LQR) mechanism is designed to achieve formation tracking while protecting agents' private control preferences. Rather than protecting the full local cost function, the proposed privacy formulation focuses on the ratio of the LQR weighting matrices, which captures the trade-off between tracking accuracy and control effort when the quadratic cost structure is publicly known. A set-theoretic sensitivity analysis shows that this weighting-ratio adjacency formulation yields less conservative privacy bounds than gradient-based protection under the considered addition/removal adjacency relation. Theoretical analysis demonstrates that, under suitable design conditions, the proposed mechanism provides bounded cumulative (\epsilon,\delta)-differential privacy guarantees for the weighting ratios over an infinite horizon without artificial noise injection. Meanwhile, the cooperative tracking error is shown to converge almost surely and in mean square to a finite random limit, with its expectation remaining bounded. Numerical examples validate the theoretical results and illustrate the resulting privacy-performance trade-off.

eess.SY↗

arXiv2026

Communication-Efficient Distributed Learning with Differential Privacy

We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that guarantees the privacy of the agents' data. The first goal is achieved through a local training approach, which reduces communication frequency. The second goal is achieved by perturbing gradients during local training, specifically through gradient clipping and additive noise. We prove that the resulting algorithm converges to a stationary point of the problem within a bounded distance. Additionally, we provide theoretical privacy guarantees within a differential privacy framework that ensure agents' training data cannot be inferred from the trained model shared over the network. We show the algorithm's superior performance on a classification task under the same privacy budget, compared with state-of-the-art methods.

cs.LG↗

arXiv2026

Can Inherent Communication Noise Guarantee Privacy in Distributed Cooperative Control ?

This paper investigates privacy-preserving distributed cooperative control for multi-agent systems within the framework of differential privacy. In cooperative control, communication noise is inevitable and is usually regarded as a disturbance that impairs coordination. This work revisits such noise as a potential privacy-enhancing factor. A linear quadratic regulator (LQR)-based framework is proposed for agents communicating over noisy channels, \textcolor{black}{where the noise variance depends on the relative state differences between neighbouring agents.} The resulting controller achieves formation while protecting the reference signals from inference attacks. It is analytically proven that the inherent communication noise can guarantee bounded $(\epsilon,\delta)$-differential privacy without adding dedicated privacy noise, while the \textcolor{black}{system cooperative tracking error} remains bounded and convergent in both the mean-square and almost-sure sense.

eess.SY↗

arXiv2025

Distributed Finite-Horizon Optimal Control for Consensus with Differential Privacy Guarantees

This paper addresses the problem of privacy-preserving consensus control for multi-agent systems (MAS) using differential privacy. We propose a novel distributed finite-horizon linear quadratic regulator (LQR) framework, in which agents share individual state information while preserving the confidentiality of their local pairwise weight matrices, which are considered sensitive data in MAS. Protecting these matrices effectively safeguards each agent's private cost function and control preferences. Our solution injects consensus error-dependent Laplace noise into the communicated state information and employs a carefully designed time-dependent scaling factor in the local cost functions. {This approach guarantees bounded consensus and achieves rigorous $\epsilon$-differential privacy for the weight matrices without relying on specific noise distribution assumptions.} Additionally, we analytically characterize the trade-off between consensus accuracy and privacy level, offering clear guidelines on how to enhance consensus performance through appropriate scaling of the LQR weight matrices and the privacy budget.

eess.SY↗