This work proposes two approaches, i.e., Fed-KD and Fed-TKD, to enhance communication efficiency with federated learning (FL) in Industrial Internet of Things (IIoT) radar sensing systems, which face the challenge in transmitting large volumes of sensitive data while still ensuring privacy. Fed-KD leverages knowledge distillation to transfer knowledge from complex teacher networks to simpler student models in order to reduce communication overhead in bandwidth-constrained environments. Fed-TKD improves communication efficiency further by applying tensor decomposition to reduce parameter redundancy. Experimental results using an industrial IoT radar imagery dataset show that both methods can significantly reduce communication costs while maintaining a high model accuracy, making them especially suitable for privacy-preserving industrial IoT sensing applications. Furthermore, experimental results with both IID and non-IID data distributions confirm the robustness of the proposed methods in heterogeneous environments.

Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prototype learning framework, named DFPL, which significantly improves the performance of DFL under heterogeneous data distributions. Specifically, DFPL introduces prototype learning into DFL to mitigate the impact of statistical heterogeneity and reduces the amount of parameters exchanged between clients. Additionally, blockchain is embedded into our framework, enabling the training and mining processes to be executed locally on each client. From a theoretical perspective, we analyze the convergence of DFPL by modeling the required computational resources during both training and mining. The experiment results highlight the superiority of DFPL in both model performance and communication efficiency across four benchmark datasets with heterogeneous data distributions.

Federated learning (FL), a distributed computing paradigm, is vulnerable to poisoning attacks that impair model performance and privacy attacks that leak participant information. Existing FL defense schemes struggle to counter poisoning attacks under data heterogeneity and high privacy computation overhead, limiting the practicality of federated learning. To address these issues, this paper proposes a model-contrastive federated learning framework with lightweight privacy preservation and poisoning attack detection, named MCFL. Specifically, we design a novel model-contrastive term by aligning intermediate-layer representations of models in the local optimization function to promote consistency of model updates among benign participants. Additionally, we design a secure aggregation protocol that adopts two-server aggregation instead of the single server to resist poisoning attacks with lightweight privacy protection. The proposed MCFL is theoretically proven in terms of convergence, robustness, and privacy. Extensive experiments demonstrate the superiority of MCFL compared to existing FL defense schemes.

In this paper, we propose an innovative pricing mechanism for cloud-edge collaborative computing resources that combines Stackelberg differential games with combinatorial double auction. The scenario of trading heterogeneous computing resources between cloud data centers, edge servers and users is modeled as a two-stage game. The Stackelberg game equilibrium is solved by Hamilton-Jacobi-Bellman (HJB) equation to optimize the resource allocation and pricing between data centers and edge servers. Markov game with multi-agent reinforcement learning is used to ensure the optimal bidding strategy of users, while differential privacy mechanism is introduced to protect participants’ sensitive information. Experimental results show that the edge server utility is improved by at least 50% and the user utility by 30% compared to the baseline algorithm. The mechanism accelerates the convergence of the game process while protecting the privacy of auction participants, providing a novel and efficient solution to the resource allocation challenge in dynamic computing environments.

Deep learning (DL) has been widely utilized in automatic modulation classification (AMC), and its performance depends largely on the presence of high-quality datasets. Motivated by this fact, this work addresses the AMC challenges in data-limited IoT environments, proposing a framework combining few-shot meta-learning and federated learning for resource-constrained devices, where edge nodes use meta-learning for training with global updates via federated averaging (FedAvg). The system aggregates samples from multiple nodes while still maintaining data security. Simulations involved 11 modulation types with varying SNRs, 100 client nodes, and 10 rounds of federated learning. The iterative process includes loading pre-trained parameters, performing local training, averaging local parameters, and updating global parameters. The obtained results show 70% post-training testing accuracy, with a consistently good performance during federated iterations. The results demonstrated the effectiveness of the proposed framework in data-scarce IoT scenarios, offering a robust performance across varying signal qualities while minimizing energy consumption and communication overhead, which is crucial for IoT device longevity and network scalability, highlighting framework’s potential for real-world applications in distributed modulation recognition.

Noncooperative computer systems and network confrontation present a core challenge in cyberspace security. Traditional cybersecurity technologies predominantly rely on passive response mechanisms, which exhibit significant limitations when addressing real-world complex and unknown threats. This paper introduces the concept of “active cybersecurity,” aiming to enhance network security not only through technical measures but also by leveraging strategy-level defenses. The core assumption of this concept is that attackers and defenders, in the context of network confrontations, act as rational decision-makers seeking to maximize their respective objectives. Building on this observation, this paper integrates game theory to analyze the interdependent relationships between attackers and defenders, thereby optimizing their strategies. Guided by this foundational idea, we propose an active cybersecurity model involving intelligent threat sensing, in-depth behavior analysis, comprehensive path profiling, and dynamic countermeasures, termed SAPC, designed to foster an integrated defense capability encompassing threat perception, analysis, tracing, and response. At its core, SAPC incorporates theoretical analyses of adversarial behavior and the optimization of corresponding strategies informed by game theory. By profiling adversaries and modeling confrontation as a “game,” the model establishes a comprehensive framework that provides both theoretical insights into and practical guidance for cybersecurity. The proposed active cybersecurity model marks a transformative shift from passive defense to proactive perception and confrontation. It facilitates the evolution of cybersecurity technologies toward a new paradigm characterized by active prediction, prevention, and strategic guidance.