Industrial Internet of Things (IIoT) has emerged as a critical infrastructure for safety monitoring in high-risk industrial environments such as petrochemical plants and power systems. Real-time identification of safety risks, including abnormal behaviors and hazardous events, is essential for ensuring operational reliability and preventing accidents. Federated learning (FL), a distributed machine learning paradigm, enables collaborative model training across multiple edge devices while preserving data privacy. However, its application in IIoT-based safety risk identification still faces considerable challenges, including unreliable edge nodes, heterogeneous computing and communication capabilities, and high communication overhead due to frequent parameter exchanges. These issues lead to degraded model accuracy, reduced system robustness, and increased latency. To address these limitations, this paper proposes a dynamic clustering framework for FL (DCF-FL) to improve the reliability and efficiency of safety risk identification in IIoT environments.

The proposed DCF-FL framework integrates three key components: a reputation evaluation mechanism, dynamic cluster head selection, and joint optimization of device selection and resource allocation. First, a comprehensive reputation evaluation mechanism based on subjective logic is designed to assess the trustworthiness of edge devices. This mechanism incorporates multiple factors—such as data contribution, local model accuracy, and channel conditions—to dynamically select reliable devices as cluster heads, thereby enhancing training robustness. Second, the FL process is organized into dynamically formed clusters, where the cluster head coordinates local training and model aggregation. A dynamic replacement strategy for cluster heads is introduced to mitigate single-point failures and adapt to varying network conditions. Third, the joint optimization problem of device participation and resource allocation is formulated as a mixed-integer nonlinear programming problem, which considers communication and computation costs while maintaining model accuracy. To efficiently solve this complex problem, a deep reinforcement learning (DRL)-based decoupling approach is adopted, where device selection and resource allocation are handled in separate stages. The DRL agent iteratively learns optimal decision policies through interaction with the environment, improving resource utilization and reducing the overall system cost.

Extensive simulations are conducted using public datasets, including BoWFire and UCF-Crime, along with a custom dataset for industrial safety scenarios. The experimental results demonstrate that DCF-FL substantially outperforms the FedAvg and FedProx baseline methods. In a typical scenario with 15 devices, DCF-FL's total communication and computation cost is reduced by 76.9% and 61.3%, respectively, compared with the baseline algorithms. Moreover, the DRL-based optimization achieves near-optimal performance, with only a 4.6% gap compared with the theoretical optimum obtained by traditional branch-and-cut methods, while maintaining much lower computational complexity. In terms of model performance, DCF-FL exhibits faster convergence and lower test loss, especially in non-independent and identically distributed data settings. The accuracy of risk identification remains consistently higher than that of the baseline methods across most training rounds, demonstrating the effectiveness of dynamic clustering and reputation-based node selection. Additionally, the proposed reputation mechanism effectively distinguishes unreliable devices, preventing their negative impact on model training and improving system stability.

This paper presents DCF-FL, a novel FL framework tailored for industrial safety risk identification in IIoT environments. By integrating subjective logic-based reputation evaluation, adaptive cluster head selection, and DRL-driven resource optimization, DCF-FL effectively addresses the challenges of node unreliability and high resource consumption in traditional FL systems. The results indicate that DCF-FL achieves a favorable balance between model accuracy and system efficiency, making it suitable for resource-constrained and heterogeneous industrial scenarios. Future work will focus on further improving the stability of model accuracy under highly dynamic conditions and extending the framework to more complex real-world applications.

In disaster emergencies, edge cloud service resource pre-caching faces two major challenges. First, post-disaster service demands exhibit strong burstiness and temporal dynamics. Insufficient pre-cached resources may cause request queuing and prolonged response latency, whereas excessive pre-caching wastes computing resources and incurs additional operating costs. Second, because disaster events are rare, historical data are insufficient for newly affected regions, new service types, and newly deployed edge nodes. Conventional prediction and reinforcement learning methods therefore struggle to obtain effective pre-caching policies rapidly under few-sample conditions. To address these challenges, this paper proposes an edge cloud service resource pre-caching method based on meta-reinforcement learning (Meta-RL).

Because user behavior data from real disaster scenarios are scarce, this paper analyzes service traffic in routine operational settings and trains the agent through interactions with daily service environments. The goal is to learn transferable workload evolution patterns and adapt quickly to emergency service modes. User request traffic is analyzed across Internet data centers (IDCs) and service types. The results show that traffic variations among IDCs exhibit correlations at monthly and intraday scales, indicating transferable common structures across regional workloads. Meanwhile, differences in request baselines, peak intensities, and fluctuation ranges reveal environmental heterogeneity. Across service types, different services share temporal trends but differ in request scale, peak duration, dispersion degree, and tail-load distribution. These observations indicate that daily service scenarios provide transferable experience, whereas data distribution differences hinder independent reinforcement learning training. Therefore, a Meta-RL framework is constructed to learn transferable policy initialization parameters through multitask training.

In the proposed framework, the edge cloud service resource pre-caching process is formulated as a Markov decision process. The agent determines the number of resources to pre-cache in the next time slot based on the current resource queue length and request waiting queue length. The reward function jointly constrains redundant cached resources and waiting requests, guiding the agent to balance service responsiveness and resource efficiency. To evaluate this trade-off, a comprehensive pooling indicator is designed from request hit capability and resource waste rate. The hit rate measures the ability of pre-cached resources to immediately satisfy user requests, whereas the waste rate characterizes the proportion of cached resources that remain unused. For policy optimization, proximal policy optimization (PPO) is adopted as the basic framework, and a meta proximal policy optimization (METAPPO) algorithm is developed. METAPPO performs task-specific adaptation through inner-loop updates and optimizes shared initial policy parameters via outer-loop updates, improving adaptability to new IDCs, service types, and emergency tasks.

Simulation experiments compare METAPPO with PPO and other baseline methods across edge cloud service environments. The results show that METAPPO accelerates convergence across multiple scenarios. After a scenario change, METAPPO still exhibits favorable convergence performance, demonstrating that the meta-learning mechanism improves policy adaptation efficiency. Although some conventional methods may achieve competitive steady-state performance after sufficient training, they usually require more historical samples and longer training processes. Conversely, METAPPO leverages cross-task experience from daily service scenarios and rapidly generates effective resource pre-caching policies under few-sample conditions, supporting fast resource allocation adaptation in multiregion, multiservice, and newly emerging emergency edge cloud scenarios.