In this work, we devise an output feedback second-order sliding mode (SOSM) control protocol for a category of nonlinear systems with unmatched uncertainties and asymmetric output constraints. The procedure of the design and analysis for this control tactic primarily encompasses the following steps. To begin with, a nonlinear mapping is introduced to convert the output-constrained SOSM system into a new SOSM dynamics without output constraints. Next, through adopting a scaling transformation with properly scheduled gains, a SOSM controller via output feedback is explicitly constructed. Third, it is theoretically certified that the presented control strategy insures both the finite-time convergence of the whole system and the compliance with the preset asymmetric output constraint. Eventually, the simulation studies on a practical application example confirm the feasibility and benefits of the proposed algorithm. Distinct from the prevailing outcomes in the literature, our control frame reveals two compelling features. One is that the output constraint issue is tackled via the system conversion based control. The other is that the proposed output feedback control design is independent of the separation principle, i.e., the finite-time convergence for the total closed-loop SOSM system embracing a state feedback SOSM controller and a discontinuous observer is together analyzed. Note to Practitioners—The paper considers a class of asymmetric output-constrained nonlinear systems subject to unmatched uncertainties, whose models are pervasively adopted in the engineering fields, e.g., manufacturing sector, transportation, etc. For such systems, the output feedback SOSM control issue hitherto has not been tackled, since under the circumstance that merely the output information is obtainable, the unmatched uncertainties and the output constraints are simultaneously considered. To this end, we successfully establish a novel output feedback SOSM control scheme by introducing the nonlinear mapping, the scaling transformation and the backstepping-like technique. Moreover, the finite-time convergence of the entire system is analyzed and corroborated via the Lyapunov method, and the fulfillment of the pre-specified asymmetric output constraint is assured, which make the proposed strategy more engineering-oriented. In future work, the efforts will be directed towards the validation of the proposed approach on an experimental platform for electric vehicles.

Considerable efforts have been devoted to addressing distributed backdoor attacks in federated learning (FL) systems. While significant progress has been made in enhancing the security of FL systems, our study reveals that there remains a false sense of security surrounding FL. We demonstrate that colluding malicious participants can effectively execute backdoor attacks during the FL training process, exhibiting high sparsity and stealthiness, which means they can evade common defense methods with only a few attack iterations. Our research highlights this vulnerability by proposing aCollusiveBackdoorAttack namedCoBA.CoBAis designed to enhance the sparsity and stealthiness of backdoor attacks by offering trigger tuning to facilitate learning of backdoor training data, controlling the bias of malicious local model updates, and applying the projected gradient descent technique. By conducting extensive empirical studies on 5 benchmark datasets, we make the following observations: 1)CoBAsuccessfully circumvents 15 state-of-the-art defense methods for robust FL; 2) Compared to existing backdoor attacks,CoBAconsistently achieves superior attack performance; and 3)CoBAcan achieve persistent poisoning effects through significantly sparse attack iterations. These findings raise substantial concerns regarding the integrity of FL and underscore the urgent need for heightened vigilance in defending against such attacks.

Are Federated Learning (FL) systems free from backdoor poisoning with the arsenal of various defense strategies deployed? This is an intriguing problem with significant practical implications regarding the utility of FL services. Despite the recent flourish of poisoning-resilient FL methods, our study shows that carefully tuning the collusion between malicious participants can minimize the trigger-induced bias of the poisoned local model from the poison-free one, which plays the key role in delivering stealthy backdoor attacks and circumventing a wide spectrum of state-of-the-art defense methods in FL. In our work, we instantiate the attack strategy by proposing a distributed backdoor attack method, namely Cerberus Poisoning (CerP). It jointly tunes the backdoor trigger and controls the poisoned model changes on each malicious participant to achieve a stealthy yet successful backdoor attack against a wide spectrum of defensive mechanisms of federated learning techniques. Our extensive study on 3 large-scale benchmark datasets and 13 mainstream defensive mechanisms confirms that Cerberus Poisoning raises a significantly severe threat to the integrity and security of federated learning practices, regardless of the flourish of robust Federated Learning methods.