Malicious clients participating in data collection and interaction may launch attacks such as model and data poisoning to degrade the performance of the global model and conceal their electricity theft behaviors. Although existing studies have introduced blockchain technology to achieve decentralization, they still suffer from limited pre-aggregation validation dimensions. To address these issues, this paper proposes a blockchain-based federated learning approach with dual-verification (BFL-DV) for electricity theft detection. In the pre-aggregation stage, a multi-metric reputation-based consensus committee verification strategy is designed, which effectively mitigates the impact of malicious participants. In the post-aggregation stage, a dynamic threshold-based blockchain verification strategy is developed to counter security risks during the transmission process, which can refuse malicious global updates adaptively. Experimental results demonstrate that BFL-DV can accurately reduce the impact of all malicious clients under the data poisoning attack. Notably, across various proportions of malicious clients, the proposed framework achieves an average AUC improvement of 32.68% compared with SOTA methods, demonstrating its consistent performance advantage.
Fanghong Guo, Yaoming Lang, Shengwei Li et al.· IEEE Transactions on Smart G...· 1 citation
With the rapid growth of aerospace activities, space situational awareness (SSA) has become increasingly important for space security. Compared with conventional two-dimensional (2D) inverse synthetic aperture radar (ISAR) image sequences, three-dimensional (3D) representations provide richer structural information. In addition, novel view synthesis (NVS) supports continuous visual interpretation and helps compensate for observation gaps. However, the limited angular coverage of single-pass observations makes stable 3D reconstruction and high-quality NVS difficult without accurate geometric calibration. To address these challenges, Radar Neural Splatting (RNSplat) is proposed for 3D reconstruction and NVS from ISAR image sequences. Specifically, a Pose Head Adaptation via Reprojection (PHARE) module is introduced to refine viewpoint parameters under a cross-view reprojection consistency constraint, thereby improving the estimation of a 3D point map. Together with the associated geometric attributes, the estimated point map is then used to construct the 3D Gaussian splatting (3DGS) representation. To better reflect ISAR image formation characteristics, a Rendering Stabilization Unit (RSU) is further introduced, using a Gaussian-sinc kernel as a PSF-based scattering response approximation to improve cross-view synthesis quality. Experimental results demonstrate that the proposed framework improves NVS performance and enhances visible structural consistency. Ablation studies further show that PHARE improves geometric consistency and 3D point map quality, while RSU enhances the consistency and quality of synthesized views.
Huayong Tang, Guolin Ma, Dongcheng Li et al.· IEEE Transactions on Computa...· 0 citations