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Limei Lin

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12 papers

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TCS Journal 2025 Journal Article

Cyclic diagnosability of folded hypercubes under the PMC model and MM* model

Linxiao Wang
Liang Chen
Kaineng Guan
Yanze Huang
Limei Lin

Cyclic diagnosability ensures reliable fault detection while maintaining loop-based connectivity, which is vital for fault-tolerant systems like data centers. Unlike traditional methods, it guarantees fault identification even if the surviving network splits into at least two cyclic components, improving resilience in connectivity-critical scenarios. With the growing need for reliable interconnection networks in large-scale multiprocessor systems, this paper explores the cyclic diagnosability of n -dimensional folded hypercubes F Q n under the PMC and MM* models. Leveraging the structural advantages of F Q n, such as its complementary edges and enhanced connectivity, we develop a systematic method to determine its cyclic diagnosability. Our analysis shows that for n ≥ 10, the cyclic diagnosability c t ( F Q n ) reaches 5 n − 5, a significant improvement over the 5 n − 10 limit of classical hypercubes. This result highlights the superior fault tolerance of folded hypercubes in maintaining loop-based connectivity during fault diagnosis.

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IJCAI Conference 2025 Conference Paper

FedCPD: Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation

Kaili Jin
Li Xu
Xiaoding Wang
Sun-Yuan Hsieh
Jie Wu
Limei Lin

Federated learning, as a distributed learning framework, aims to develop a global model while preserving client privacy. However, heterogeneity of client data leads to fairness issues and reduced performance. Techniques like parameter decoupling and prototype learning appear promising, yet challenges such as forgetting historical data and limited generalization persist. These methods also lack local insights, with locally trained features prone to overfitting, which affects generalization in global parameter aggregation. To address these challenges, we propose FedCPD, a personalized federated learning framework. FedCPD maintains historical information, reduces information loss, and increases personalization through hierarchical feature distillation and cross-layer feature fusion. Moreover, we utilize representation techniques like prototype contrastive learning and prototype alignment to capture diverse client data features, thus improving model generalization and fairness. Experiments show FedCPD outperforms state-of-the-art models, enhancing generalization by up to 10. 40% and personalization by up to 4. 90%, highlighting its effectiveness and superiority.

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IJCAI Conference 2025 Conference Paper

FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients

Xiaoding Wang
Bin Ye
Li Xu
Lizhao Wu
Sun-Yuan Hsieh
Jie Wu
Limei Lin

Federated learning is vulnerable to model poisoning attacks in which malicious participants compromise the global model by altering the model updates. Current defense strategies are divided into three types: aggregation-based methods, validation dataset-based methods, and update distance-based methods. However, these techniques often neglect the challenges posed by device heterogeneity and asynchronous communication. Even upon identifying malicious clients, the global model may already be significantly damaged, requiring effective recovery strategies to reduce the attacker's impact. Current recovery methods, which are based on historical update records, are limited in environments with device heterogeneity and asynchronous communication. To address these problems, we introduce FedHAN, a reliable federated learning algorithm designed for asynchronous communication and device heterogeneity. FedHAN customizes sparse models, uses historical client updates to impute missing parameters in sparse updates, dynamically assigns adaptive weights, and combines update deviation detection with update prediction-based model recovery. Theoretical analysis indicates that FedHAN achieves favorable convergence despite unbounded staleness and effectively discriminates between benign and malicious clients. Experiments reveal that FedHAN, compared to leading methods, increases the accuracy of the model by 7. 86%, improves the detection accuracy of poisoning attacks by 12%, and enhances the recovery accuracy by 7. 26%. As evidenced by these results, FedHAN exhibits enhanced reliability and robustness in intricate and dynamic federated learning scenarios.

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IJCAI Conference 2025 Conference Paper

RepObE: Representation Learning-Enhanced Obfuscation Encryption Modular Semantic Task Framework

Limei Lin
Jinpeng Xu
Xiaoding Wang
Liang Chen
Sun-Yuan Hsieh
Jie Wu

Model inversion and adversarial attacks in semantic communication pose risks, such as content leaks, alterations, and prediction inaccuracies, which threaten security and reliability. This paper introduces, from an attacker's viewpoint, a novel framework called RepObE (Representation Learning-Enhanced Obfuscation Encryption Modular Semantic Task Framework) to secure semantic communication. This framework employs dynamic encryption during semantic extraction and feature transmission to hinder attackers from reconstructing data through eavesdropping, thus strengthening system privacy. To combat image communication task challenges, we propose a prototype adversarial collaborative alignment training approach enhanced by representation learning. This method extracts and encodes semantic features while using dynamic perturbation and robust optimization to improve system resilience against adversarial threats. The approach ensures reliable semantic communication in complex environments, maintaining performance while countering attacks using feature obfuscation, adversarial training, and representation learning. Experimental results demonstrate that our method surpasses existing techniques by more than 2% in resisting model inversion attacks on classification tasks. Visually, our method excels with minimal decipherable images for attackers. It also shows a 3% to 5% improvement in countering adversarial attacks on classification tasks.

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TCS Journal 2021 Journal Article

The t/s-diagnosability and t/s-diagnosis algorithm of folded hypercube under the PMC/MM* model

Yuhang Lin
Limei Lin
Yanze Huang
Jiaru Wang

A t / s -diagnosable system refers to such a system that all the faulty nodes of the system can be isolated within a set of size at most s in the presence of at most t faulty nodes. Moreover, it increases the allowed faulty nodes, hence enhancing the diagnosability of the system. We can find that the t / s -diagnosability of n-dimensional folded hypercube F Q n has not been studied under the PMC model and MM* model. In this paper, we determine the t / s -diagnosability of F Q n under the PMC model and MM* model. First, we propose some new fault tolerant properties of F Q n. Then we prove that the t / s -diagnosability of n-dimensional folded hypercube F Q n is ( n + 1 ) g − 1 2 ( g − 1 ) ( g + 2 ) for 2 ≤ g ≤ 1 2 ( n − 1 ) where s = ( n + 1 ) g − 1 2 ( g − 1 ) ( g + 2 ) + g − 2 under both the PMC model and MM* model. In addition, we establish two t / s -diagnosis algorithms of complexity O ( N l o g 2 N ) and complexity O ( N ( l o g 2 N ) 2 ) to isolate the faulty nodes in a node subset of the system under the PMC model and MM* model, respectively. The comparison analysis results showed that the t / s -diagnosability of F Q n is the largest, and it increases faster than the other types of diagnosability as n increases.

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TCS Journal 2020 Journal Article

A new proof for exact relationship between extra connectivity and extra diagnosability of regular connected graphs under MM* model

Yanze Huang
Limei Lin
Li Xu

The extra connectivity and extra diagnosability are two important measures for network reliability. Under MM* model, two possible relationships between extra connectivity and extra diagnosability were proposed in Reference [24]. However, there are some shortcomings in it: (1) the conclusion of Theorem 3. 9 is wrong; (2) the corresponding proof of Theorem 3. 9 is flawed; (3) the exact relationship is still not clear. In this paper, we impose reasonable constraints, and give a new and correct proof for measuring the exact relationship between h-extra connectivity κ h ( G ) and h-extra diagnosability t h m ˜ ( G ) of the regular connected graph G under MM* model, which is t h m ˜ ( G ) = κ h ( G ) + h. As an application, we directly obtain the h-extra diagnosability of star graph S n, alternating group graph network A N n and ( n, k ) -star graph S n, k by our proposed new result.

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TCS Journal 2020 Journal Article

Restricted connectivity and good-neighbor diagnosability of split-star networks

Limei Lin
Yanze Huang
Xiaoding Wang
Li Xu

The restricted connectivity and the g-good-neighbor diagnosability are two important indicators of the robustness for a multi-processor system in presence of failing processors. The g-good-neighbor diagnosability of a graph guarantees that the number of fault-free neighbors of every fault-free vertex is greater or equal to g in the graph. We first establish the 3-restricted connectivity of an n-dimensional split-star network S n 2. Then we propose the upper bound of the { 1, 2, 3 } -good-neighbor diagnosability of S n 2 under the MM* model. Moreover, we show that when deleting two indistinguishable good-neighbor faulty vertex-sets from S n 2, the remaining connected subgraph has no isolated vertex. Furthermore, we give a complete proof for the lower bound of the { 1, 2, 3 } -good-neighbor diagnosability of S n 2, and prove that the lower and upper bounds of the { 1, 2, 3 } -good-neighbor diagnosability of S n 2 are accurate.

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TCS Journal 2019 Journal Article

Extra diagnosability and good-neighbor diagnosability of n-dimensional alternating group graph AG under the PMC model

Yanze Huang
Limei Lin
Li Xu
Xiaoding Wang

The h-extra diagnosability and g-good-neighbor diagnosability are two important diagnostic strategies at system-level that can significantly enhance the system's self-diagnosing capability. The h-extra diagnosability ensures that every component of the system after removing a set of faulty vertices has at least h + 1 vertices. The g-good-neighbor diagnosability guarantees that after removing some faulty vertices, every vertex in the remaining system has at least g neighbors. In this paper, we analyze the extra diagnosability and good-neighbor diagnosability in a well-known n-dimensional alternating group graph A G n proposed for multiprocessor systems under the PMC model. We first establish that the 1-extra diagnosability of A G n ( n ≥ 5 ) is 4 n − 10. Then we prove that the 2-extra diagnosability of A G n ( n ≥ 5 ) is 6 n − 17. Next, we address that the 3-extra diagnosability of A G n ( n ≥ 5 ) is 8 n − 25. Finally, we obtain that the g-restricted connectivity and the g-good-neighbor diagnosability of A G n ( n ≥ 5 ) are ( 2 g + 2 ) n − 2 g + 2 − 4 + g and ( 2 g + 2 ) n − 2 g + 2 − 4 + 2 g for 1 ≤ g ≤ 2, respectively.

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TCS Journal 2018 Journal Article

On the reliability of alternating group graph-based networks

Yanze Huang
Limei Lin
Dajin Wang

The probability of having faults in a multiprocessor computer system increases as the size of system grows. One way to quantify the reliability of a system is using the probability that a fault-free subsystem of a certain size still exists with the presence of individual faults. The higher the probability is, the more reliable the system is. In this paper, we establish the reliability for networks based on A G n, the n-dimensional alternating group graph. More specifically, we calculate the probability of a subnetwork (or subgraph) A G n n − 1 being fault-free, when given a single node's fault probability. Since subnetworks of A G n intersect in highly complex manners, our scheme is to use the Principle of Inclusion–Exclusion to obtain a lower-bound of the probability, by considering intersections of up to four subgraphs. We show that the lower-bound derived this way is very close to the upper-bound obtained in a previous result, which means the lower-bound we get is a very tight one. Therefore, both lower-bound and upper-bound are close approximations of the accurate probability.

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TCS Journal 2016 Journal Article

Relating the extra connectivity and the conditional diagnosability of regular graphs under the comparison model

Limei Lin
Li Xu
Shuming Zhou

Extra connectivity and conditional diagnosability are two crucial subjects for a multiprocessor system's ability to tolerate and diagnose faulty processors. The extra connectivity and the conditional diagnosability of many well-known multiprocessor systems have been widely investigated. In this paper, the relationship between the extra connectivity and the conditional diagnosability of regular graphs is explored. We establish that the conditional diagnosability under the comparison model is equal to the 2-extra connectivity. Finally, we give empirical analysis on the extra connectivity and conditional diagnosability of some graphs by our proposed relationship.

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TCS Journal 2015 Journal Article

Conditional diagnosability and strong diagnosability of Split-Star Networks under the PMC model

Limei Lin
Li Xu
Shuming Zhou

An interconnection network's diagnosability is an important measure of its self-diagnostic capability. The classical problems of fault diagnosis are explored widely. The conditional diagnosability is proposed by Lai et al. as a new measure of diagnosability, which can better measure the diagnosability of regular interconnection networks. The conditional diagnosability is an important indicator of the robustness of a multiprocessor system in presence of failed processors. Furthermore, a multiprocessor system is strongly t-diagnos-able, if it is t-diagnosable and can achieve diagnosability t + 1 except for the case where a node's neighbors are all faulty. The conditional diagnosability and strong diagnosability were proposed later to better reflect the networks' self-diagnostic capability under more realistic assumptions. In this paper, we determine the conditional diagnosability of an n-dimensional Split-Star Network (denoted as S n 2 ), a well-known interconnection network model for multiprocessor systems, under the PMC (Preparata, Metze, and Chien) model. We show that the conditional diagnosability of S n 2 ( n ≥ 4 ) is 8 n − 23, which is about four times of its traditional diagnosability. As a byproduct, the strong diagnosability of S n 2 is also obtained.

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TCS Journal 2014 Journal Article

Conditional diagnosability of arrangement graphs under the PMC model

Limei Lin
Shuming Zhou
Li Xu
Dajin Wang

Processor fault diagnosis has played an important role in measuring the reliability of a multiprocessor system, and the diagnosabilities of many well-known multiprocessor systems have been investigated. The conditional diagnosability has been widely accepted as a measure of diagnosability by assuming an additional condition that any fault-set can't contain all the neighbors of any node in a multiprocessor system. This paper considers the conditional diagnosability of an ( n, k ) -arrangement graph A n, k, a flexible interconnection network model for multiprocessor systems, under the classical PMC diagnostic model, and determines that the conditional diagnosability of A n, k ( k ≥ 2, n ≥ k + 2 ) is ( 4 k − 4 ) ( n − k ) − 3, which is about four times its traditional diagnosability.

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