This article focuses on a decentralized online optimization problem over multiagent systems, where the interactions are modeled by a strongly connected directed graph. The objective of each agent is to minimize the global loss function accumulated by all agents' local loss functions, which are time-varying and only known by themselves. To address the communication bottleneck caused by the high-dimensional data and large-scale networks, we design a decentralized online algorithm with compressed communication, decentralized online gradient push-sum with compressed communication (CC-DOGPS). For strongly convex functions, a sublinear regret bound $\mathcal {O}((\ln T)^{2})$ of our designed algorithm is obtained, where $T$ is the time horizon. Finally, two numerical simulations are given to validate the theoretical results and illustrate the efficiency of our designed algorithm.

In 3D inverse synthetic aperture radar (ISAR) imaging of space targets, radar neural radiance fields (i.e., ISARNeRF) is an important research direction. However, a significant yet unexplored problem is its deployment in continual learning scenarios, where gradual 3D imaging is expected since target ISAR images often emerge sequentially. To address this issue, this letter proposes a new continual 3D ISAR imaging method, named CL-ISAR-NeRF. Specifically, CL-ISAR-NeRF leverages a multilevel distillation mechanism to simultaneously replay pixel, field, and feature-levels information, to alleviate the forgetting of previously learned knowledge. In addition, an efficient memory selection strategy is designed to enrich the diversity of lineof-sight (LOS) when selecting replayed data, which improves imaging performance and further enhances the stability and plasticity of the method. In order to evaluate the proposed method in continual learning settings, we design a realistic simulation scenario in which the trajectories of space targets are calculated by the Simplified General Perturbations-4 (SGP4) model. The comparative experiments with classic continual learning methods demonstrate the superior performance and robustness of CLISAR-NeRF

This article focuses on the online distributed optimization with privacy protection in time-varying unbalanced networks. We consider the case where there exist potential passive attackers in the network, having access to all communication channels. The attackers attempt to deduce the privacy of participating nodes. In this case, the differential privacy approach is leveraged to enable privacy protection. Based on this privacy-preserving approach and the stochastic subgradient method, a novel differentially private distributed online stochastic subgradient descent (DP-DOSSD) algorithm is devised for addressing the considered problem. Unlike the existing algorithms that require the weight matrices to be doubly stochastic, our algorithm only depends on two different weight matrices that are column-stochastic and row-stochastic, respectively. Moreover, a stochastic subgradient rescaling technique is adopted to tackle the unbalancedness of time-varying directed networks. It proves that our algorithm not only guarantees ϵ-differential privacy but also establishes an expected regret of orderO(√T) in convex settings, where ϵ andTare the privacy level and the time horizon, respectively. The established result matches the optimal regret bound derived by state-of-the-art algorithms. The fundamental tradeoff between convergence performance and privacy level is also studied. Finally, simulation results for the sensor network-based online distributed estimation problem and the distributed online ridge regression problem are provided to confirm the effectiveness of our approach.

We focus on the problem of distributed online constrained convex optimization with statistical privacy in multiagent systems. The participating agents aim to collaboratively minimize the cumulative system-wide cost while a passive adversary corrupts some of them. The passive adversary collects information from corrupted agents and attempts to estimate the private information of the uncorrupted ones. In this scenario, we adopt a correlated perturbation mechanism with globally balanced property to cover the local information of agents to enable privacy preservation. This work is the first attempt to integrate such a mechanism into the distributed online (sub)gradient descent algorithm, and then a new algorithm called privacy-preserving distributed online convex optimization (PP-DOCO) is designed. It is proved that the designed algorithm provides a statistical privacy guarantee for uncorrupted agents and achieves an expected regret in $\mathcal {O}(\sqrt {K})$ for convex cost functions, where K denotes the time horizon. Furthermore, an improved expected regret in $\mathcal {O}(\log (K))$ is derived for strongly convex cost functions. The obtained results are equivalent to the best regret scalings achieved by state-of-the-art algorithms. The privacy bound is established to describe the level of statistical privacy using the notion of Kullback-Leibler divergence (KLD). In addition, we observe that a tradeoff exists between our algorithm's expected regret and statistical privacy. Finally, the effectiveness of our algorithm is validated by simulation results.

This article focuses on the problem of fuzzy-model-based fault-tolerant control for nonlinear semi-Markov jump systems in the discrete-time context. The discrete-time semi-Markov process is employed to describe the mode jumping among several subsystems in the investigated systems. Moreover, the nonlinear characteristics are effectively tackled through utilizing the Takagi–Sugeno fuzzy model. Unlike some previous results, the information of the semi-Markov kernel (SMK) in this article is assumed to be partially available, which conforms better with the practical scenarios. Besides, considering the case that actuators may encounter some unexpected failures during system operation, the fault-tolerant mechanism is introduced in the process of controller design to enhance the fault tolerance of the studied systems. By means of SMK approach and Lyapunov stability theory, some elapsed-time-dependent criteria for guaranteeing the mean-square stability of the closed-loop system are established. According to these criteria, the design methodology of the reliable fuzzy state feedback controller is developed. Eventually, in order to verify the practicability and rationality of the developed control scheme, a single-link robot arm model is presented.