Graph Neural Networks (GNNs) have demonstrated superior performance in processing centralized graph-structured data. However, real-world privacy and security concerns hinder data centralization and shareing, leading to severe data isolation (data silos). While Federated Learning (FL) offers a distributed solution to mitigate these obstacles, existing Federated Graph Neural Network (FedGNN) frameworks struggle to effectively address data heterogeneity. To address this, this paper proposes DA-DFGAS, a federated graph neural architecture search algorithm. Specifically, DA-DFGAS facilitates model personalization via a directed tree topology and path constraint mechanisms, while simultaneously employing a joint self-attention mechanism based on predicted probability distributions to capture distributional variations across multiple clients. Furthermore, it integrates a bi-level global-local objective optimization strategy to ensure global model consistency while preserving local adaptability. Experimental results on multiple datasets demonstrate that DA-DFGAS outperforms state-of-the-art methods, achieving 0.5–3.0% accuracy improvements over centralized baselines and 0.5–5.0% over federated counterparts.

The growing demand for high-precision neural network training and inference has driven the necessity for floating-point (FP) compute-in-memory (CIM) architectures. However, compared to the extensively studied INT-CIM, the energy efficiency of FP-CIM still requires further optimization and enhancement. This work presents an energy-efficient multiply-less digital SRAM-based FP-CIM architecture. Specifically, to improve the energy efficiency and minimize the area requirement, we propose to employ logarithmic approximate FP multiplication (LAM) within the FP-CIM architecture. The LAM approximates FP multiplication by converting it into a straightforward addition operation, thereby reducing the power consumption and area. Additionally, we propose an approximate adder with error-reduced compensation to address critical path delay issues associated with carry propagation, further minimizing power consumption and area overhead. A 24Kb SRAM CIM macro with the proposed techniques is designed in a 28nm CMOS technology and occupies an area of 0.033 mm2. The simulation results show that our work achieves an energy efficiency of 19.7 TFLOPS/W with bfloat16 representation at 0.9V and 200MHz.

Graph-based clustering is a basic subject in the field of machine learning, but most of them still have the following deficiencies. First, similarity graph construction and data division into corresponding classes are always divided into two independent steps. Second, noise contained in real data may cause the learned similarity graph to be inaccurate. Third, the traditional metrics based on Euclidean distance is difficult to tackle non-Gaussian noise. In order to eliminate these limitations, a noise-aware clustering based on correntropy and adaptive graph regularization method (NCCAGR) is proposed. 1) In order to change the problem from two-steps to single-step, we formulate a joint clustering learning framework that simultaneously learns a robust similarity graph and performs data clustering; 2) To overcome the influence of noise, we construct a Laplacian matrix and perform adaptive graph regularization based on clean data; 3) By introducing the correntropy to solve the problem of non-Gaussian noise and heavy tail in the original data. Furthermore, a half-quadratic optimization method is used to transform the problem into a quadratic form to facilitate subsequent solutions. Finally, experiments show that the proposed method not only has high performance, but also outperforms both classical methods and state-of-the-art methods in robustness.

Multi-view clustering is an important technique in machine learning research. Existing methods have improved in clustering performance, most of them learn graph structure depending on all samples, which are high complexity. Bipartite graph-based multi-view clustering can obtain clustering result by establishing the relationship between the sample points and small anchor points, which improve the efficiency of clustering. Most bipartite graph-based clustering methods only focus on topological graph structure learning depending on sample nodes, ignore the influence of node features. In this paper, we propose bipartite graph-based discriminative feature learning for multi-view clustering, which combines bipartite graph learning and discriminative feature learning to a unified framework. Specifically, the bipartite graph learning is proposed via multi-view subspace representation with manifold regularization terms. Meanwhile, our feature learning utilizes data pseudo-labels obtained by fused bipartite graph to seek projection direction, which make the same label be closer and make data points with different labels be far away from each other. At last, the proposed manifold regularization terms establish the relationship between constructed bipartite graph and new data representation. By leveraging the interactions between structure learning and discriminative feature learning, we are able to select more informative features and capture more accurate structure of data for clustering. Extensive experimental results on different scale datasets demonstrate our method achieves better or comparable clustering performance than the results of state-of-the-art methods.

Clustering has been applied in machine learning, data mining and so on, and has received extensive attention. However, since some data has noise or outliers, these noise or outliers easily bring about the objective function with large errors. In this paper, a robust clustering model with ℓ 2 , 1 , ℓ 1 norm and Laplacian regularization (RCLR) is proposed, on which, sparse error matrix is introduced to express sparse noise, and ℓ 1 norm is introduced to alleviate the sparse noise. In addition, the ℓ 2 , 1 norm is also introduced to achieves space robust by virtue of its nice rotation invariance property. Therefore, our RCLR is insensitive to data noise and outliers. More importantly, the Laplacian regularization is introduced into the RCLR to improve the clustering accuracy. In order to solve the optimization objective of clustering problem, we propose an iterative updating algorithm, named alternating direction method of multipliers (ADMM), to update each optimization variable alternatively, and the convergence of the proposed algorithm is also proved in theory. Finally, experimental results on a total of eleven datasets of three types of datasets, elaborate the superiority of this method over six existing classical clustering methods. Three types of datasets include face images dataset, handwritten recognition dataset, and UCI dataset. In particular, our RCLR clustering approach has the best effect on face image dataset.

For millimeter-wave (MMW) communications, the in-phase quadrature imbalance (IQI) at the transmitter (TX) and the receiver (RX) may severely degrade the system performance, if not compensated. This paper addresses the joint estimation and compensation of TX and RX IQIs for single carrier frequency domain equalization (SC-FDE) systems. In particular, we introduce a concept called channel variation energy (CVE) to characterize the smoothness of channel frequency response observed at the receiver. By minimizing the CVE, the expressions relating the TX and RX IQIs are derived in closed form. Based on the expressions obtained, two schemes are proposed to estimate the TX and RX IQIs: i) Rosenbrock search based scheme; 2) least-square (LS) scheme. Once the IQIs are estimated, the channel information can be easily obtained by simple substitution. Therefore, the proposed schemes can separately estimate TX IQI, RX IQI and channel information, which is necessary for SC-FDE. Through simulations compliant with IEEE 802.11ad standard, it is shown that the proposed schemes can achieve the performance close to the ideal case without IQIs.