arXiv subjects

Tong Cheng

Publications and source records attributed to Tong Cheng.

5 recordsLinked to original sources

arXiv2025

LEFT-RS: A Lock-Free Fault-Tolerant Resource Sharing Protocol for Multicore Real-Time Systems

Emerging real-time applications have driven the transition to multicore embedded systems, where tasks must share resources due to functional demands and limited availability. These resources, whether local or global, are protected within critical sections to prevent race conditions, with locking protocols ensuring both exclusive access and timing requirements. However, transient faults occurring within critical sections can disrupt execution and propagate errors across multiple tasks. Conventional locking protocols fail to address such faults, and integrating traditional fault tolerance techniques often increases blocking. Recent approaches improve fault recovery through parallel replica execution; however, challenges remain due to sequential accessing, coordination overhead, and susceptibility to common-mode faults. In this paper, we propose a Lock-frEe Fault-Tolerant Resource Sharing (LEFT-RS) protocol for multicore real-time systems. LEFT-RS allows tasks to concurrently access and read global resources while entering their critical sections in parallel. Each task can complete its access earlier upon successful execution if other tasks experience faults, thereby improving the efficiency of resource usage. Our design also limits the overhead and enhances fault resilience. We present a comprehensive worst-case response time analysis to ensure timing guarantees. Extensive evaluation results demonstrate that our method significantly outperforms existing approaches, achieving up to an 84.5% improvement in schedulability on average.

cs.OS↗

arXiv2025

scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering

Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due to the high dimensionality and prevalence of zero elements in scRNA-seq data, traditional clustering methods face significant statistical and computational challenges. While some advanced methods use graph neural networks to model cell-cell relationships, they often depend on static graph structures that are sensitive to noise and fail to capture the long-tailed distribution inherent in single-cell populations.To address these limitations, we propose scAGC, a single-cell clustering method that learns adaptive cell graphs with contrastive guidance. Our approach optimizes feature representations and cell graphs simultaneously in an end-to-end manner. Specifically, we introduce a topology-adaptive graph autoencoder that leverages a differentiable Gumbel-Softmax sampling strategy to dynamically refine the graph structure during training. This adaptive mechanism mitigates the problem of a long-tailed degree distribution by promoting a more balanced neighborhood structure. To model the discrete, over-dispersed, and zero-inflated nature of scRNA-seq data, we integrate a Zero-Inflated Negative Binomial (ZINB) loss for robust feature reconstruction. Furthermore, a contrastive learning objective is incorporated to regularize the graph learning process and prevent abrupt changes in the graph topology, ensuring stability and enhancing convergence. Comprehensive experiments on 9 real scRNA-seq datasets demonstrate that scAGC consistently outperforms other state-of-the-art methods, yielding the best NMI and ARI scores on 9 and 7 datasets, respectively.Our code is available at Anonymous Github.

cs.LG↗

arXiv2025

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression

Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from various clients. Although FGL allows client data to remain localized, a malicious server can still steal client private data information through uploaded gradient. In this paper, we for the first time propose label distribution attacks (LDAs) on FGL that aim to infer the label distributions of the client-side data. Firstly, we observe that the effectiveness of LDA is closely related to the variance of node embeddings in GNNs. Next, we analyze the relation between them and propose a new attack named EC-LDA, which significantly improves the attack effectiveness by compressing node embeddings. Then, extensive experiments on node classification and link prediction tasks across six widely used graph datasets show that EC-LDA outperforms the SOTA LDAs. Specifically, EC-LDA can achieve the Cos-sim as high as 1.0 under almost all cases. Finally, we explore the robustness of EC-LDA under differential privacy protection and discuss the potential effective defense methods to EC-LDA. Our code is available at https://github.com/cheng-t/EC-LDA.

cs.LG↗

arXiv2024

FedFDP: Fairness-Aware Federated Learning with Differential Privacy

Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persistent issues related to fairness and data privacy. To tackle these challenges simultaneously, we propose a fairness-aware federated learning algorithm called FedFair. Building on FedFair, we introduce differential privacy to create the FedFDP algorithm, which addresses trade-offs among fairness, privacy protection, and model performance. In FedFDP, we developed a fairness-aware gradient clipping technique to explore the relationship between fairness and differential privacy. Through convergence analysis, we identified the optimal fairness adjustment parameters to achieve both maximum model performance and fairness. Additionally, we present an adaptive clipping method for uploaded loss values to reduce privacy budget consumption. Extensive experimental results show that FedFDP significantly surpasses state-of-the-art solutions in both model performance and fairness.

cs.CR↗

arXiv2020

A Study on Nice Open Covers in Constructive Analysis

Mathematicians like Markov and Bishop made an effort to develop constructive mathematics and extended many theorems in classical mathematical analysis. Heine Borel theorem tells us that a closed bounded subset of Euclidean space R is compact, but in constructive mathematics, Tseitin and Zaslavskii showed that the set of all constructive real numbers between 0 and 1 is not compact. We are going to show that when giving certain restriction to the open cover on [0,1], we can however always choose a finite sub-cover.

math.LO↗