Despite its success in many applications, federated learning is increasingly vulnerable to sophisticated poisoning attacks. Existing defenses, particularly Byzantine Robust Aggregation Rules (BRARs), offer some protection but rely on strong assumptions or challenging technical prerequisites. To address these shortcomings, we propose anensemble defense with provably convergent aggregation(EndPCA). By using the entropy weight method to consolidate scores from multiple BRARs into an ensemble trust score, it effectively integrates heterogeneous weak BRARs to resist a wide range of poisoning attacks under practical assumptions. We formally prove that EndPCA can provide theoretical guarantees of convergence with bounded error. Our empirical evaluations show that EndPCA consistently outperforms existing BRARs, demonstrating its effectiveness across various scenarios.

Abstract Federated Learning (FL) enables multiple clients to collaboratively train a shared model without exposing local data, making it a fundamental paradigm for large-scale distributed intelligence. However, in practical edge-cloud deployments, the server must inspect a large volume of high-dimensional client updates within tight communication windows, which makes secure aggregation a problem closely tied to parallel processing, real-time response, and high-performance computing (HPC) resources. Among the major threats to FL, backdoor attacks are particularly insidious because they implant malicious behaviors into the global model while preserving benign-task performance. Although pre-aggregation defenses based on gradient analysis are promising, the current state-of-the-art methods such as Scope suffer from two key limitations: fixed parameters are ineffective against adaptive attackers, and single-point clustering is vulnerable to failure under heterogeneous (non-IID) data distributions. To address these limitations, we propose FAROS, a robust and HPC-friendly defense framework that generalizes the transform-and-cluster paradigm. FAROS incorporates two key components: Adaptive Differential Scaling (ADS), which dynamically adjusts defense sensitivity according to the dispersion of client gradients, and Robust Core-set Computing (RCC), which replaces single-point clustering with a consensus-based centroid derived from a stable core-set. This design improves robustness while preserving server-side efficiency through vectorizable similarity computation and parallelizable filtering. Extensive experiments on multiple datasets, models, and attack settings show that FAROS consistently outperforms existing defenses in both attack suppression and benign-task accuracy, while remaining compatible with scalable distributed FL infrastructures.

Multi-agent path finding (MAPF) is a safety-critical scenario where the goal is to secure collision-free trajectories from initial to desired locations. However, due to system complexity and uncertainty, integrating learning-based controllers with MAPF is challenging and cannot theoretically guarantee the safety of the learned controllers. In response, our study proposes a verified safe multi-agent neural control (VSMANC) approach for MAPF, focusing on the unified training of Decentralized Control Barrier Functions (DCBF) and controllers to enhence safety. VSMANC enables all agents to concurrently learn controllers and DCBFs using a unified loss function designed to maximize safety, adhere to standard control policies, and incorporate path-finding-related heuristics. We also propose a formal verification-guided retraining process to both verify the properties of the learned DCBFs and generate counterexamples for retraining, thereby providing a verified safety guarantee. We validate our approach through shape formation experiments and UAV simulations, demonstrating significant improvements in safety and effectiveness in complex multi-agent environments.

Social media platforms such as Twitter, Reddit, and Sina Weibo playa crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation frame-work using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework's effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios. The reproduction kit can be accessed at https://github.com/BlueLinkXlGA-MAS.

Tactical Data Link (TDL) is a complex, specialised system that supports the construction of communication applications. To navigate its complexity, model-based system engineering (MBSE), especially Unified Modeling Language (UML)-based modelling, has emerged as the leading approach in developing TDL-specific systems. However, TDL domain experts often find UML modelling notably challenging. That significantly hinders their full engagement in TDL engineering. To bridge this gap, we introduce DSL-MoLab, a tailored framework of DSL-enabled model-based development toolkit that empowers TDL domain experts to engage with the MBSE process of TDL-specific systems straightforwardly. DSL-MoLab encompasses: a domain-specific language (DSL), TDL-DSL, that incorporates TDL-specific concepts and notations fully understood by TDL domain experts; a TDL-DSL Editor that offers both graphical and command-line interfaces for interactive modelling; a UML2DSL Translator and a DSL2UML Translator that jointly facilitate bidirectional translation between UML and DSL models; and a TDL-Code Generator that converts UML models into executable programs leveraging ANTLR for the process. Additionally, DSL-MoLab utilises WebAssembly to support lightweight service deployment, allowing for running on various OS architectures. Applying this framework to a case study within Link 16 demonstrates its effectiveness in enabling TDL domain experts to significantly contribute to engineering TDL systems straightforwardly.

Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modeled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrate the applicability and potency in mitigating security risks.

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI’s within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies. †

Recent years have seen a growing interest in deep learning-based approaches to localize faults in software. However, existing methods have not reached a satisfying level of accuracy. The main reason is that the feature extraction of faulty code elements is insufficient. Namely, these deep learning-based methods will learn some features that are not relevant to fault localization, and thus ignore the features related to fault localization. We propose SupConFL, a new framework for statement-level fault localization. Our framework combines the statement-level abstract syntax tree with the statement sequence, and adopt controllable attention-based LSTM to locate the faulty elements. The training is done through contrastive learning between the faulty code and its fixed version. By comparing the faulty code with the fixed code, the model can learn richer features of the faulty code elements. Our experiments on Defects4j-1.2.0 dataset show that our method outperforms the current state-of-the-art. Specifically, SupConFL improves Top-1 score by 7.96% in comparison with the current state-of-the-art. In addition, our method has also achieved good results in cross-project experiments.

Decision making in self-adaptive systems often involves trade-offs between multiple quality attributes, with user preferences that indicate the relative importance and priorities among the attributes. However, eliciting such preferences accurately from users is a difficult task, as they may find it challenging to specify their preference in a precise, mathematical form. Instead, they may have an easier time expressing their displeasure when the system does not exhibit behaviors that satisfy their internal preferences. Furthermore, the user’s preference may change over time depending on the environmental context; thus, the system may be required to continuously adapt its behavior to satisfy this change in preference. However, existing self-adaptive frameworks do not explicitly consider dynamic human preference as one of the sources of uncertainty. In this paper, we propose a new adaptation framework that is specifically designed to support self-adaptation to user preference. Our framework takes a human-on-the-loop approach where the user is given an ability to intervene and indicate dissatisfaction and corrections with the current behavior of the system; in such a scenario, the system automatically updates the existing preference values so that the new, resulting behavior of the system is consistent with the user’s notion of satisfactory behavior. To perform this adaptation, we propose a novel similarity analysis to produce changes in the preference that are optimal with respect to the system utility. We illustrate our approach in a case study involving a delivery robot system. Our preliminary results indicate that our approach can effectively adapt its behavior to changing human preference.

Recent advances of consensus control have made it significant in multi-agent systems such as in distributed machine learning, distributed multi-vehicle cooperative systems. However, during its application it is crucial to achieve resilience and privacy; specifically, when there are adversary/faulty nodes in a general topology structure, normal agents can also reach consensus while keeping their actual states unobserved. In this article, we modify the state-of-the-art Q-consensus algorithm by introducing predefined noise or well-designed cryptography to guarantee the privacy of each agent state. In the former case, we add specified noise on agent state before it is transmitted to the neighbors and then gradually decrease the value of noise so the exact agent state cannot be evaluated. In the latter one, the Paillier cryptosystem is applied for reconstructing reward function in two consecutive interactions between each pair of neighboring agents. Therefore, multi-agent privacy-preserving resilient consensus (MAPPRC) can be achieved in a general topology structure. Moreover, in the modified version, we reconstruct reward function and credibility function so both convergence rate and stability of the system are improved. The simulation results indicate the algorithms’ tolerance for constant and/or persistent faulty agents as well as their protection of privacy. Compared with the previous studies that consider both resilience and privacy-preserving requirements, the proposed algorithms in this article greatly relax the topological conditions. At the end of the article, to verify the effectiveness of the proposed algorithms, we conduct two sets of experiments, i.e., a smart-car hardware platform consisting of four vehicles and a distributed machine learning platform containing 10 workers and a server.

Reinforcement learning is a powerful methodology that enables self-adaptive systems to relearn and update their adaptation policy when dealing with unforeseen changes. To update the policy more efficiently, several knowledge reuse approaches have been proposed to speed up relearning. However, the current studies treat and reuse the knowledge integrally, which may result in increased relearning costs if the reused knowledge is inappropriate in the changed situation. Generally, some localized pieces of the knowledge are still appropriate for reuse if they are not related to the changes, while some pieces may become inappropriate for reuse if they are affected by the changes. This paper proposes a goal-oriented curriculum evolution method to realize finer-grained knowledge reuse, combining goal-oriented modeling and curriculum learning. The method is twofold: (1) at design time, we apply goal-oriented modeling to design a curriculum in which an RL problem is decomposed into sub-problems, so that knowledge can be decomposed into several pieces of localized knowledge for sub-problems, and (2) at runtime, we evolve the curriculum to reflect changes (i.e., update the sub-problems related to the changes), so that the affected pieces of knowledge can be locally updated to make them appropriate for reuse in the changed situation. The evaluation based on a cleaning robot shows that the relearning time was shortened, demonstrating the effectiveness of our method.

Distributed deep reinforcement learning(DDRL) has been used in distributed systems to better improve the adaptability. However, DDRL-based systems are also inevitably under the threat of Byzantine workers. There is an urgent need to enhance the resilience of the DDRL-based system against Byzantine failures. This paper proposes a resilient mechanism for mitigating the influence of Byzantine workers on DDRL-based systems. First, we formalize the DDRL-based system as a multi-armed bandit model for well capturing the collective effect of workers on the whole learning process, and then transforming the resilient mechanism design problem into the sampling policy optimization problem. Second, we propose a self-adaptation process for filtering out the harmful data generated by Byzantine workers and theoretically give a mathematical analysis of the understanding, demonstrating its effectiveness under ideal conditions. Third, based on a typical DDRL-based system (i.e., Asynchronous Advantage Actor-Critic, A3C), we implement a resilient distributed A3C (ReD-A3C). With extensive experiments on the DDRL benchmark tasks, we show that ReD-A3C outperforms available Byzantine tolerant approaches.

Smart spaces such as smart homes deliver digital services to optimize space use and enhance user experience. They are composed of an Internet of Things (IoT), people, and physical content. They differ from traditional computer systems in that their cyber-physical nature ties intimately with the users and the built environment. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash, risking potentially physical harm to the space and its users. Ensuring smart space safety is therefore critically important to successfully deliver intimate and convenient services surrounding our daily lives. By modeling smart space as a highly dynamic database, we present IoT Transactions, an analogy to database transactions, as an abstraction for programming and executing the services as the handling of the devices in smart space. Unlike traditional database management systems that take a “clear room approach,” smart spaces take a “dirty room approach” where imperfection and unattainability of full control and guarantees are the new normal. We identify Atomicity, Isolation, Integrity and Durability (AI 2 D) as the set of properties necessary to define the safe runtime behavior for IoT transactions for maintaining “permissible device settings” of execution and to avoid or detect and resolve “impermissible settings.” Furthermore, we introduce a lock protocol, utilizing variations of lock concepts, that enforces AI 2 D safety properties during transaction processing. We show a brief proof of the protocol correctness and a detailed analytical model to evaluate its performance.

Abstract Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. Game theory approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity ofcomponents. Under security attacks, the architecture model of the system is translated into aBayesian multi-player game, where each component is explicitly modeled as an independent player while security attacks are encoded as variant types for the components. The optimal defensive strategy for the system is dynamically computed by solving the pure equilibrium (i.e., adaptation response) to achieve the best possible system utility, improving the resiliency of the system against security attacks. We illustrate our approach using an example involving load balancing and a case study on inter-domain routing.