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Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, the inherent collaborative nature of FL still poses privacy risks. This paper investigates the integration of differential privacy (DP) into the FL framewor...
This paper investigates the crucial aspect of quantum error correction (QEC) – the propagation of information during encoding and decoding processes. We propose a novel methodology utilizing Monte Carlo simulation to model the probability distribution of information diffusion within QEC schemes. The core claim is that...
This paper presents a novel framework for formal verification of generative models, focusing on ensuring their stability and preventing the generation of undesirable outputs. Generative models, such as GANs and diffusion models, are increasingly prevalent in various applications, yet their inherent complexity makes the...
This paper investigates the crucial aspect of quantum error correction (QEC) – the propagation of information during encoding and decoding processes. We propose a novel methodology utilizing Monte Carlo simulation to model the probability distribution of information diffusion within QEC schemes. The core claim is that...
Temporal graph embeddings aim to capture the evolving behavior of graphs over time, a crucial task in domains like social network analysis, knowledge graph reasoning, and anomaly detection. However, current graph embedding techniques often treat temporal relationships as simple sequential adjacency updates, neglecting...
Temporal graph embeddings aim to capture the evolving behavior of graphs over time, a crucial task in domains like social network analysis, knowledge graph reasoning, and anomaly detection. However, current graph embedding techniques often treat temporal relationships as simple sequential adjacency updates, neglecting...
This paper investigates the design and implementation of a novel information diffusion suppression mechanism tailored for topological networks. Traditional information diffusion models often assume uniform network structures, failing to account for the inherent advantages offered by networks with specific topologies. T...
This paper investigates the design and implementation of a novel information diffusion suppression mechanism tailored for topological networks. Traditional information diffusion models often assume uniform network structures, failing to account for the inherent advantages offered by networks with specific topologies. T...
Quantum-Enhanced Diffusion Modeling with Adaptive Kernel (QEDM-K) presents a novel approach to diffusion modeling by integrating quantum-inspired kernels to address limitations of existing methods. This research explores the application of these kernels to improve the accuracy and speed of simulating diffusion processe...
Graph neural networks (GNNs) have achieved significant success in various graph-related tasks, including node classification, link prediction, and graph classification. However, a critical limitation of many GNN approaches is their dependence on large amounts of labeled data for training. Obtaining such labeled data ca...
This paper proposes a novel approach to cybersecurity threat detection leveraging Graph Neural Networks (GNNs) for personalized modeling. Traditional cybersecurity solutions often rely on generic, rule-based systems that struggle to adapt to the diverse and evolving nature of network environments. We introduce a framew...
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