Implicit poisoning in federated learning is a significant threat, with malicious nodes subtly altering gradient parameters each round, making detection difficult. This study investigates this problem, revealing that temporal analysis alone struggles to identify such covert attacks, which can bypass online methods like cosine similarity and clustering. Common detection methods rely on offline analysis, resulting in delayed responses. However, recalculating gradient updates reveals distinct characteristics of malicious clients. Based on this finding, we designed a privacy-preserving detection algorithm using trajectory anomaly detection. Singular values of matrices are used as features, and an improved Isolation Forest algorithm processes these to detect malicious behavior. Experiments on MNIST, FashionMNIST, and CIFAR-10 datasets show our method achieves 94.3% detection accuracy and a false positive rate below 1.2%, indicating its high accuracy and effectiveness in detecting implicit model poisoning attacks.

With the increasing use of wireless sensor networks in water quality monitoring, an enormous amount of streaming data is generated by widely deployed sensors. However, the current batch mode used for data analysis can no longer meet the diverse combination of monitoring indicators and the requirement for timely analysis results on an all-weather basis. To overcome these challenges and analyze a large amount of water quality data quickly and accurately, we propose a stream-DBSCAN distributed stream processing clustering model. First, real-time data streams are processed using the distributed stream computing framework Flink. Then, the DBSCAN clustering algorithm is applied to cluster each dataset as a different dimension of the cluster. Finally, the time distribution characteristics of the data in the same cluster are analyzed to identify the water quality variation rules. The system can extract data noise points and identify sudden deterioration of water quality. We tested the model using datasets on three water quality indices, pH, ammonia nitrogen (NH₄N), and turbidity, in the Yantai Menlou Reservoir from May to August 2019. The results demonstrate that the system can efficiently and quickly perform cluster analysis on streaming data. By analyzing the clustering results, we found that the daily variation of water quality and sudden pollution events in the Menlou Reservoir are consistent with the actual situation.

Currently, blockchain is facing a serious storage explosion problem. While most storage scaling schemes are focused on permissionless blockchain, we propose the ring-overlap mechanism for consortium blockchain and use it to scale Hyperledger Fabric. In our scheme, all accounting nodes are divided into clusters, and each cluster contains several nodes; then, a portion of the entire block data is stored in a cluster. Block data is stored overlappingly on some cluster nodes, and each block is guaranteed to have some copies in a cluster. Theoretical analysis and simulation show that the storage occupied by nodes is significantly reduced in blockchain applications with frequent transactions, and the mechanism can still guarantee data integrity in the case of partial node failures in a single cluster. Furthermore, for transaction-frequent applications, storage space consumption can be significantly reduced without increasing excessive query time overhead.