Industrial time series often display complex, non-stationary behaviors with trends, periodicity, and abrupt fluctuations. Generating high-quality synthetic data in such domains is essential for simulation, forecasting, and anomaly detection in Industrial Internet of Things (IIoT) applications. However, distributional heterogeneity, sparse failure patterns, and long-term dependencies make this task highly challenging. We introduce HNRF-TS, a rectified flow framework with hybrid noise initialization, designed for scalable and robust time series generation. The hybrid prior combines isotropic Gaussian noise with structured codes from a lightweight generative adversarial network (GAN), yielding semantically aligned and diverse latent representations. To improve sampling efficiency, we propose a bimodal adaptive strategy that allocates denser ordinary differential equation (ODE) steps at the beginning and end of the trajectory while using coarser steps in smoother middle regions. This preserves critical temporal features while lowering computational cost. We further enhance fidelity with modules dedicated to modeling trends and seasonality, which capture global drifts and periodic signals inherent in industrial data. Across multiple IIoT datasets, HNRF-TS outperforms state-of-the-art baselines, including GAN-based and diffusion-based methods. It achieves up to 75.8% reduction in Context-FID and over 60% improvement in correlation metrics on long-horizon tasks. Moreover, high-quality samples can be generated with as few as 20 sampling steps, offering significant efficiency gains without sacrificing accuracy.

With the rapid development of cutting-edge technologies such as software-defined networking, edge computing, and deep learning (DL), the application of the field of Industrial Internet of Things (IIoT) has been deepening, especially in the areas of fault diagnosis, defect detection, and production management, which has shown great potential. Federated learning (FL) is a collaborative model training approach that allows multiple clients to work together while maintaining data privacy. This method is particularly useful for DL methods in industrial surface defect classification, which often require a large amount of training data that can be hard to gather due to its distributed nature across various sources. However, the aggregated model in federated learning may not perform well when there is a discrepancy between the training dataset (source domain) and the testing dataset (target domain), as well as when individual users face data scarcity. To counter these challenges, we propose a novel federated transfer learning framework with multi-scale aggregation (FTL-MSA) for surface defect classification in the IIoT system. A dynamic central loss function, which takes into account both intra-instance and inter-instance contrasting, is proposed to enhance the model's accuracy. Furthermore, we introduce a multi-scale model aggregation technique for FL. This technique considers the distances between the source domain and the target domain at multiple scales, which utilizes the Jensen-Shannon distance for statistical consistency, and the cosine distance for directional consistency, thereby effectively mitigating the impacts of domain differences. Empirical validation on two public steel defect datasets shows that our FTL-MSA framework outperforms state-of-the-art methods, achieving accuracy improvements of 3.12%-12.51%.

Blockchain technology has shown great application potential in many fields such as finance, supply chain, and the Internet of Things. As blockchain technology keeps evolving, the issue of insufficient scalability has progressively turned into a crucial bottleneck impeding its further progress. The application of sharding technology to enhance the scalability of blockchain systems has attracted considerable interest from the research community. However, the majority of sharding solutions suffer from a number of problems, including high ratio of cross-shard transactions, prolonged transaction confirmation latency, and unbalanced load among shards. To solve these problems, we propose a blockchain sharding method based on dual-constraint label propagating. This sharding method mainly depends on two constraints in the label propagation process to determine whether to perform partition transfers on blockchain accounts. Constraint 1 is to consider the correlation among accounts when splitting accounts. This constraint serves to minimize the cross-shard transactions ratio. Constraint 2 is that the load factor of the blockchain sharding system after the tag update cannot exceed the load factor before the update. This can bring the load of each shard to an almost consistent state, thereby reducing transaction confirmation latency. To verify the effectiveness of our proposed method, we compare it with the Metis and Monoxide algorithms in three aspects: cross-shard transaction ratio, transaction throughput, and transaction confirmation latency. Furthermore, we examine the influence of the load distribution across individual shards within the blockchain network on both throughput and transaction confirmation latency. The experimental results show that our proposed method makes the load among shards more balanced, thereby reducing the ratio of cross-shard transactions, decreasing the transaction confirmation latency, and increasing the transaction throughput.

Federated learning (FL) has become a promising machine learning (ML) paradigm for training machine learning models over distributed datasets, owing to its low communication costs and privacy preserving property. To date, the most commonly adopted model fusion mechanism in FL is average aggregation. However, it has been shown that this average aggregation mechanism performs poorly in heterogeneous systems, especially for non-independent and identically distributed (NonIID) data. In order to address this challenge, we propose a weighted FL model aggregation strategy for each client based on clustering, termed CluFL. Specifically, CluFL first measures the similarities among uploaded models from clients through their parameters using a spectral clustering algorithm. Then, CluFL assigns aggregation weights according to the similarity of the intra-cluster global model for each cluster and the average model across the clusters. Further, we derive a convergence bound on the CluFL algorithm considering a practical nonconvex setting of neural network training. This bound reveals that the proposed CluFL algorithm can achieve a convergence speed in the order of O(1/T). Extensive experiments have been conducted on both FashionMNIST and CIFAR-10 datasets and show that CluFL outperforms the state-of-the-art FL algorithms in terms of accuracy and communication efficiency.