Personalized Federated Learning (PFL), which aims to customize models for each client while preserving data privacy, has become an important research topic in addressing the challenges of data heterogeneity. Existing studies usually enhance the localization of global parameters by injecting local information into the globally shared model. However, these methods focus excessively on the personalized characteristics of individual clients and fail to fully exploit distinctive information across clients, limiting the quality of local models to represent unseen samples well. To address this issue, we propose a novel personalized Federated Privacy-preserving Knowledge Dynamic Alignment (FedPKDA) framework, which ensures data privacy during both the collection of client-side key information and its incorporation into federated model training. Specifically, to ensure data privacy during the cross-client information collection phase, we first conduct feature clipping and add Laplacian noise to the local prototypes extracted from each client. Further, we compute the centroid of the uploaded local prototypes in a latent space and leverage Mahalanobis distance to guide the generation of global prototypes, thereby preserving the semantic contributions from participating clients. Moreover, to boost the personalization of the local model, we dynamically align representations learned by the shared model with both a set of local prototypes and privacy-preserving global prototypes, facilitating effective cross-client knowledge sharing under heterogeneous settings while preserving client-specific characteristics. Extensive experiments on benchmark datasets have verified the superiority of FedPKDA against its competitors.

Deep graph clustering, as a fundamental task in data mining, has attracted widespread attention. Recently, excellent performance has been achieved by integrating graph structure and node attributes to generate consensus latent embeddings. However, existing clustering methods are limited by redundant information and unreliable clustering distribution, which hinders the discriminative power of the latent embeddings. To address this issue, we propose a novel deep graph clustering framework called Multi-dimensional Topological Association Strengthening Clustering Network (MTASCN). Specifically, we design a Multi-dimensional Feature Association Mechanism (MFAM), which extracts the competitive or cooperative relationship between features to alleviate the interference of redundant features and enhance the dominant features. In addition, we develop a Structure-oriented Multi-order Loss Module (SMLM) that reinforces the generation of clustering distribution under reliable structure information guidance by calculating the multi-order similarity between the latent embeddings and the original graph structure. Extensive experiments on five benchmark datasets have demonstrated that MTASCN consistently outperforms other clustering methods.

Membership inference (MI) attacks pose a significant threat to user privacy in machine learning systems. While numerous attack mechanisms have been proposed in the literature, the lack of standardized evaluation parameters and metrics has led to inconsistent and even conflicting comparison results. To address this issue and facilitate a systematic analysis of these disparate findings, we introduce MIBench, a comprehensive benchmark that includes a suite of carefully designed evaluation scenarios (ESs) and evaluation metrics to provide a consistent framework for assessing the efficacy of various MI techniques. The ESs are crafted to encompass four critical factors: intra-dataset distance distribution, inter-sample distance within the target dataset, differential distance analysis, and inference withholding ratio. In total, MIBench includes ten typical evaluation metrics and incorporates 84 distinct ESs for each dataset. Using MIBench, we conducted a thorough comparative analysis of 15 state-of-the-art MI attacks across 588 ESs, seven widely adopted datasets, and seven representative model architectures. Our analysis revealed 83 instances of Conflicting Comparison Results (CCR), providing substantial evidence for the CCR Phenomenon. We identified two CCR types: Type 1 (single-factor) and Type 2 (dual-factor). The distribution of CCR instances across the four critical factors was: inter-sample distance (40.96%), differential distance (37.35%), inference withholding ratio (19.28%), and intra-dataset distance (2.41%). All MIBench codes and evaluations are available athttps://github.com/MIBench/MIBench.github.io/blob/main/README.md.